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5 pages, 208 KiB

Open AccessEditorial

Recent Advances on Optimization for Control, Observation, and Safety

by Guillermo Valencia-Palomo, Francisco-Ronay López-Estrada and Damiano Rotondo

Processes 2020, 8(2), 201; https://doi.org/10.3390/pr8020201 - 6 Feb 2020

Cited by 1 | Viewed by 2317

Abstract

Mathematical optimization is the selection of the best element in a set with respect to a given criterion [...] Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

Research

Jump to: Editorial, Review

14 pages, 4722 KiB

Open AccessArticle

Model-Based Safety Analysis for the Fly-by-Wire System by Using Monte Carlo Simulation

by Zhong Lu, Lu Zhuang, Li Dong and Xihui Liang

Processes 2020, 8(1), 90; https://doi.org/10.3390/pr8010090 - 9 Jan 2020

Cited by 12 | Viewed by 4771

Abstract

Safety analysis is one of the important means to show compliance with airworthiness requirements. The traditional safety analysis methods are significantly dependent on analysts’ skills and experiences. A model-based safety analysis approach is proposed for typical fly-by-wire (FBW) systems based on the system [...] Read more.

Safety analysis is one of the important means to show compliance with airworthiness requirements. The traditional safety analysis methods are significantly dependent on analysts’ skills and experiences. A model-based safety analysis approach is proposed for typical fly-by-wire (FBW) systems based on the system development model built via Simulink, by which the response of system performances can be simulated. The safety requirements of the FBW system are defined by presenting the thresholds of system performance metrics, and the effects of failure conditions on aircraft safety are determined according to the system response simulation by injecting failures or failure combinations into the Simulink model. The Monte Carlo simulation method is used to calculate the probability of unsafe conditions, whose effects are determined by the system response simulation with fault injections. Finally, a case study is used to illustrate the effectiveness and advantages of our proposed approach. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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10 pages, 2576 KiB

Open AccessArticle

Improved Genetic Algorithm Tuning Controller Design for Autonomous Hovercraft

by Huu Khoa Tran, Hoang Hai Son, Phan Van Duc, Tran Thanh Trang and Hoang-Nam Nguyen

Processes 2020, 8(1), 66; https://doi.org/10.3390/pr8010066 - 3 Jan 2020

Cited by 17 | Viewed by 4710

Abstract

By mimicking the biological evolution process, genetic algorithm (GA) methodology has the advantages of creating and updating new elite parameters for optimization processes, especially in controller design technique. In this paper, a GA improvement that can speed up convergence and save operation time [...] Read more.

By mimicking the biological evolution process, genetic algorithm (GA) methodology has the advantages of creating and updating new elite parameters for optimization processes, especially in controller design technique. In this paper, a GA improvement that can speed up convergence and save operation time by neglecting chromosome decoding step is proposed to find the optimized fuzzy-proportional-integral-derivative (fuzzy-PID) control parameters. Due to minimizing tracking error of the controller design criterion, the fitness function integral of square error (ISE) was employed to utilize the advantages of the modified GA. The proposed method was then applied to a novel autonomous hovercraft motion model to display the superiority to the standard GA. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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16 pages, 1145 KiB

Open AccessFeature PaperArticle

Estimation of Actuator and System Faults Via an Unknown Input Interval Observer for Takagi–Sugeno Systems

by Citlaly Martínez-García, Vicenç Puig, Carlos-M. Astorga-Zaragoza, Guadalupe Madrigal-Espinosa and Juan Reyes-Reyes

Processes 2020, 8(1), 61; https://doi.org/10.3390/pr8010061 - 2 Jan 2020

Cited by 12 | Viewed by 2940

Abstract

This paper presents a simultaneous state variables and system and actuator fault estimation, based on an unknown input interval observer design for a discrete-time parametric uncertain Takagi–Sugeno system under actuator fault, with disturbances in the process and measurement noise. The observer design is [...] Read more.

This paper presents a simultaneous state variables and system and actuator fault estimation, based on an unknown input interval observer design for a discrete-time parametric uncertain Takagi–Sugeno system under actuator fault, with disturbances in the process and measurement noise. The observer design is synthesized by considering unknown but bounded process disturbances, output noise, as well as bounded parametric uncertainties. By taking into account these considerations, the upper and lower bounds of the considered faults are estimated. The gain of the unknown input interval observer is computed through a linear matrix inequalities (LMIs) approach using the robust

H_{\infty}

criteria in order to ensure attenuation of process disturbances and output noise. The interval observer scheme is experimentally evaluated by estimating the upper and lower bounds of a torque load perturbation, a friction parameter and a fault in the input voltage of a permanent magnet DC motor. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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25 pages, 1458 KiB

Open AccessFeature PaperArticle

Economic Reliability-Aware MPC-LPV for Operational Management of Flow-Based Water Networks Including Chance-Constraints Programming

by Fatemeh Karimi Pour, Vicenç Puig and Gabriela Cembrano

Processes 2020, 8(1), 60; https://doi.org/10.3390/pr8010060 - 2 Jan 2020

Cited by 6 | Viewed by 3076

Abstract

This paper presents an economic reliability-aware model predictive control (MPC) for the management of drinking water transport networks (DWNs). The proposed controller includes a new goal to increase the system and components reliability based on a finite horizon stochastic optimization problem with joint [...] Read more.

This paper presents an economic reliability-aware model predictive control (MPC) for the management of drinking water transport networks (DWNs). The proposed controller includes a new goal to increase the system and components reliability based on a finite horizon stochastic optimization problem with joint probabilistic (chance) constraints. The proposed approach is based on a single-layer economic optimization problem with dynamic constraints. The inclusion of components and system reliability in the MPC model using an Linear Parameter Varying (LPV) modeling approach aims to maximize the availability of the system by estimating system reliability. On the other hand, the use of a LPV-MPC control approach allows the controller to consider nonlinearities in the model in a linear like way. Moreover, the resulting MPC optimization problem can be formulated as a Quadratic Programming (QP) problem at each sampling time reducing the computational burden/time compared to solving a nonlinear programming problem. The use of chance-constraint programming allows the computation of an optimal strategy with a pre-established risk acceptability levels to cope with the uncertainty of the demand forecast. Finally, the proposed approach is applied to a part of the water transport network of Barcelona for demonstrating its performance. The obtained results show that the system reliability of the DWN is maximized compared with the other approaches. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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15 pages, 1682 KiB

Open AccessArticle

Robust Fault Protection Technique for Low-Voltage Active Distribution Networks Containing High Penetration of Converter-Interfaced Renewable Energy Resources

by Shijie Cui, Peng Zeng, Chunhe Song and Zhongfeng Wang

Processes 2020, 8(1), 34; https://doi.org/10.3390/pr8010034 - 30 Dec 2019

Cited by 3 | Viewed by 2756

Abstract

With the decentralization of the electricity market and the plea for a carbon-neutral ecosystem, more and more distributed generation (DG) has been incorporated in the power distribution grid, which is then known as active distribution network (ADN). The addition of DGs causes numerous [...] Read more.

With the decentralization of the electricity market and the plea for a carbon-neutral ecosystem, more and more distributed generation (DG) has been incorporated in the power distribution grid, which is then known as active distribution network (ADN). The addition of DGs causes numerous control and protection confronts to the traditional distribution network. For instance, two-way power flow, small fault current, persistent fluctuation of generation and demand, and uncertainty of renewable energy sources (RESs). These problems are more challenging when the distribution network hosts many converter-coupled DGs. Hence, the traditional protection schemes and relaying methods are inadequate to protect ADNs against short-circuit faults and disturbances. We propose a robust communication-assisted fault protection technique for safely operating ADNs with high penetration of converter-coupled DGs. The proposed technique is realizable by employing digital relays available in the recent market and it aims to protect low-voltage (LV) ADNs. It also includes secondary protection that can be enabled when the communication facility or protection equipment fails to operate. In addition, this study provides the detail configuration of the digital relay that enables the devised protection technique. Several enhancements are derived, as alternative technique for the traditional overcurrent protection approach, to detect small fault current and high-impedance fault (HIF). A number of simulations are performed with the complete model of a real ADN, in Shenyang, China, employing the PSCAD software platform. Various cases, fault types and locations are considered for verifying the efficacy of the devised technique and the enabling digital relay. The obtained simulation findings verify the proposed protection technique is effective and reliable in protecting ADNs against various fault types that can occur at different locations. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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20 pages, 3699 KiB

Open AccessArticle

Single Controller-Based Colored Petri Nets for Deadlock Control in Automated Manufacturing Systems

by Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li and Reggie Davidrajuh

Processes 2020, 8(1), 21; https://doi.org/10.3390/pr8010021 - 22 Dec 2019

Cited by 32 | Viewed by 6079

Abstract

Deadlock control approaches based on Petri nets are usually implemented by adding control places and related arcs to the Petri net model of a system. The main disadvantage of the existing policies is that many control places and associated arcs are added to [...] Read more.

Deadlock control approaches based on Petri nets are usually implemented by adding control places and related arcs to the Petri net model of a system. The main disadvantage of the existing policies is that many control places and associated arcs are added to the initially constructed Petri net model, which significantly increases the complexity of the supervisor of the Petri net model. The objective of this study is to develop a two-step robust deadlock control approach. In the first step, we use a method of deadlock prevention based on strict minimal siphons (SMSs) to create a controlled Petri net model. In the second step, all control places obtained in the first step are merged into a single control place based on the colored Petri net to mark all SMSs. Finally, we compare the proposed method with the existing methods from the literature. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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11 pages, 2974 KiB

Open AccessArticle

Fault Diagnosis of the Blocking Diesel Particulate Filter Based on Spectral Analysis

by Shuang-xi Liu and Ming Lü

Processes 2019, 7(12), 943; https://doi.org/10.3390/pr7120943 - 10 Dec 2019

Cited by 8 | Viewed by 4394

Abstract

Diesel particulate filter is one of the most effective after-treatment techniques to reduce Particulate Matters (PM) emissions from a diesel engine, but the blocking Diesel Particulate Filter (DPF) will seriously affect the engine performance, so it is necessary to study the fault diagnosis [...] Read more.

Diesel particulate filter is one of the most effective after-treatment techniques to reduce Particulate Matters (PM) emissions from a diesel engine, but the blocking Diesel Particulate Filter (DPF) will seriously affect the engine performance, so it is necessary to study the fault diagnosis of blocking DPF. In this paper, a simulation model of an R425DOHC diesel engine with wall-flow ceramic DPF was established, and then the model was verified with experimental data. On this basis, the fault diagnosis of the blocking DPF was studied by using spectral analysis on instantaneous exhaust pressure. The results showed that both the pre-DPF mean exhaust pressure and the characteristic frequency amplitude of instantaneous exhaust pressure can be used as characteristic parameters of monitoring the blockage fault of DPF, but it is difficult to monitor DPF blockage directly by instantaneous exhaust pressure. In terms of sensitivity, the characteristic frequency amplitude of instantaneous exhaust pressure is more suitable as a characteristic parameter to monitor DPF blockage than mean exhaust pressure. This work can lay an important theoretical foundation for the on-board diagnosis of DPF. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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18 pages, 2418 KiB

Open AccessFeature PaperArticle

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

by A. Navarro, J. A. Delgado-Aguiñaga, J. D. Sánchez-Torres, O. Begovich and G. Besançon

Processes 2019, 7(12), 913; https://doi.org/10.3390/pr7120913 - 3 Dec 2019

Cited by 8 | Viewed by 2795

Abstract

This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior [...] Read more.

This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior node of the pipeline, it has been shown that observability is indeed satisfied in this case, which allows designing an observer for the unmeasurable state variables, i.e., the pressure head at leak position. Relying on the fact that the origin of the observation error is exponentially stable if all parameters (including the leak coefficients) are known and uniformly ultimately bounded otherwise, the authors propose a bank of observers as follows: taking into account that the physical pipeline parameters are well-known, and there is only uncertainty about leak coefficients (position and magnitude), a pair of such coefficients is taken from a search space and is assigned to an observer. Then, a Genetic Algorithm (GA) is exploited to minimize the integration of the square observation error. The minimum integral observation error will be reached in the observer where the estimated leak parameters match the real ones. Finally, some results are presented by using real-noisy databases coming from a test bed plant built at Cinvestav-Guadalajara, aiming to show the potentiality of this method. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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16 pages, 2989 KiB

Open AccessArticle

Generalized Proportional Model of Relay Protection Based on Adaptive Homotopy Algorithm Transient Stability

by Feng Zheng, Jiahao Lin, Jie Huang and Yanzhen Lin

Processes 2019, 7(12), 899; https://doi.org/10.3390/pr7120899 - 2 Dec 2019

Cited by 3 | Viewed by 2430

Abstract

Relay protection equipment is important to ensure the safe and stable operation of power systems. The risks should be evaluated, which are caused by the failure of relay protection. At present, the fault data and the fault status monitoring information are used to [...] Read more.

Relay protection equipment is important to ensure the safe and stable operation of power systems. The risks should be evaluated, which are caused by the failure of relay protection. At present, the fault data and the fault status monitoring information are used to evaluate the failure risks of relay protection. However, there is a lack of attention to the information value of monitoring information in the normal operation condition. In order to comprehensively improve monitoring information accuracy and reduce, a generalized proportional hazard model (GPHM) is established to fully exploit the whole monitoring condition information during the whole operation process, not just the monitoring fault condition data, with the maximum likelihood estimation (MLE) used to estimate the parameters of the GPHM. For solving the nonlinear equation in the process of parameter estimations, the adaptive homotopy algorithm is adopted, which could ensure the reversibility of the Jacobi matrix. Three testing cases have been reviewed, to demonstrate that the adaptive homotopy algorithm is better than traditional algorithms, such as the Newton homotopy algorithm, regarding the calculation speed and convergence. Therefore, GPHM could not only reflect the real time state of the equipment, but also provide a sound theoretical basis for the selection of equipment maintenance types. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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17 pages, 6228 KiB

Open AccessFeature PaperArticle

Performance Improvement of a Grid-Tied Neutral-Point-Clamped 3-φ Transformerless Inverter Using Model Predictive Control

by Hani Albalawi and Sherif A. Zaid

Processes 2019, 7(11), 856; https://doi.org/10.3390/pr7110856 - 15 Nov 2019

Cited by 12 | Viewed by 4167

Abstract

Grid-connected photovoltaic (PV) systems are now a common part of the modern power network. A recent development in the topology of these systems is the use of transformerless inverters. Although they are compact, cheap, and efficient, transformerless inverters suffer from chronic leakage current. [...] Read more.

Grid-connected photovoltaic (PV) systems are now a common part of the modern power network. A recent development in the topology of these systems is the use of transformerless inverters. Although they are compact, cheap, and efficient, transformerless inverters suffer from chronic leakage current. Various researches have been directed toward evolving their performance and diminishing leakage current. This paper introduces the application of a model predictive control (MPC) algorithm to govern and improve the performance of a grid-tied neutral-point-clamped (NPC) 3-φ transformerless inverter powered by a PV panel. The transformerless inverter was linked to the grid via an inductor/capacitor (LC) filter. The filter elements, as well as the internal impedance of the grid, were considered in the system model. The discrete model of the proposed system was determined, and the algorithm of the MPC controller was established. Matlab’s simulations for the proposed system, controlled by the MPC and the ordinary proportional–integral (PI) current controller with sinusoidal pulse width modulation (SPWM), were carried out. The simulation results showed that the MPC controller had the best performance for earth leakage current, total harmonic distortion (THD), and the grid current spectrum. Also, the efficiency of the system using the MPC was improved compared to that using a PI current controller with SPW modulation. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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16 pages, 4994 KiB

Open AccessFeature PaperArticle

Evolution of High-Viscosity Gas–Liquid Flows as Viewed Through a Detrended Fluctuation Characterization

by J. Hernández, D. F. Galaviz, L. Torres, A. Palacio-Pérez, A. Rodríguez-Valdés and J. E. V. Guzmán

Processes 2019, 7(11), 822; https://doi.org/10.3390/pr7110822 - 6 Nov 2019

Cited by 7 | Viewed by 2362

Abstract

We characterize the long-term development of high-viscosity gas–liquid intermittent flows by means of a detrended fluctuation analysis (DFA). To this end, the pressures measured at different locations along an ad hoc experimental flow line are compared. We then analyze the relevant time-series to [...] Read more.

We characterize the long-term development of high-viscosity gas–liquid intermittent flows by means of a detrended fluctuation analysis (DFA). To this end, the pressures measured at different locations along an ad hoc experimental flow line are compared. We then analyze the relevant time-series to determine the evolution of the various kinds of intermittent flow patterns associated with the mixtures under consideration. Although no pattern transitions are observed in the presence of high-viscosity mixtures, we show that the dynamical attributes of each kind of intermittence evolves from one point to another within the transport system. The analysis indicates that the loss of a long-range correlation between the pressure responses are due to the discharge processes. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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14 pages, 2238 KiB

Open AccessArticle

The Rotating Components Performance Diagnosis of Gas Turbine Based on the Hybrid Filter

by Li Zeng, Shaojiang Dong and Wei Long

Processes 2019, 7(11), 819; https://doi.org/10.3390/pr7110819 - 5 Nov 2019

Cited by 6 | Viewed by 2504

Abstract

Gas turbine converts chemical energy into mechanical energy and provide energy for aircraft, ships, etc. The performance diagnosis of rotating components of gas turbine are essential in terms of the high failure rate of these parts. A problem that the sudden changing of [...] Read more.

Gas turbine converts chemical energy into mechanical energy and provide energy for aircraft, ships, etc. The performance diagnosis of rotating components of gas turbine are essential in terms of the high failure rate of these parts. A problem that the sudden changing of operation state of turbines may lead to the misdiagnosis due to the defect of gas turbine’s model. This paper constructs the strong tracking filter based on the unscented Kalman filter to achieve accurate estimation of gas turbine’s measured parameters when the state changes suddenly. In the strong tracking filter, a parameter optimization method based on the residual similarity of measured parameters is proposed. Next, adopt the measured parameters filtered by the strong tracking filter to construct the health parameters estimation algorithm based on the particle filter. The particle weight is optimized by the mean adjustment method. Performance diagnosis is realized by checking the changes of health parameters output by particle filter. The results show that the proposed method improves the accuracy of performance diagnosis obviously. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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15 pages, 1165 KiB

Open AccessArticle

Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study

by Clara M. Ionescu, Constantin F. Caruntu, Ricardo Cajo, Mihaela Ghita, Guillaume Crevecoeur and Cosmin Copot

Processes 2019, 7(11), 778; https://doi.org/10.3390/pr7110778 - 29 Oct 2019

Cited by 11 | Viewed by 2889

Abstract

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost [...] Read more.

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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28 pages, 7281 KiB

Open AccessArticle

A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

by Seung-Jun Shin, Young-Min Kim and Prita Meilanitasari

Processes 2019, 7(10), 739; https://doi.org/10.3390/pr7100739 - 14 Oct 2019

Cited by 20 | Viewed by 4569

Abstract

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through [...] Read more.

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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14 pages, 1315 KiB

Open AccessArticle

Fine-Tuning Meta-Heuristic Algorithm for Global Optimization

by Ziyad T. Allawi, Ibraheem Kasim Ibraheem and Amjad J. Humaidi

Processes 2019, 7(10), 657; https://doi.org/10.3390/pr7100657 - 26 Sep 2019

Cited by 26 | Viewed by 4673

Abstract

This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that [...] Read more.

This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown to check the performance of each algorithm, and the other test for 30 trials to measure the statistical results of the performance of the proposed algorithm against the others. Results confirm that the proposed FTMA global optimization algorithm has a competing performance in comparison with its counterparts in terms of speed and evading the local minima. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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14 pages, 1629 KiB

Open AccessArticle

The Bilinear Model Predictive Method-Based Motion Control System of an Underactuated Ship with an Uncertain Model in the Disturbance

by Huu-Quyen Nguyen, Anh-Dung Tran and Trong-Thang Nguyen

Processes 2019, 7(7), 445; https://doi.org/10.3390/pr7070445 - 12 Jul 2019

Cited by 9 | Viewed by 5152

Abstract

Ship transportation plays an increasingly important role in and accounts for a large proportion of cargo transport. Therefore, it is necessary to improve the quality of the trajectory control system of the ship for improving the transport efficiency and ensuring maritime safety. This [...] Read more.

Ship transportation plays an increasingly important role in and accounts for a large proportion of cargo transport. Therefore, it is necessary to improve the quality of the trajectory control system of the ship for improving the transport efficiency and ensuring maritime safety. This paper deals with the advanced control system for the three-degrees-of-freedom model of the underactuated ship in the condition of uncertain disturbance. Based on the three-degrees-of-freedom model of the underactuated ship, the authors built a bilinear model of the ship by linearizing each nonlinear model section. Then, the authors used the state estimator to compensate for uncertain components and random disturbances in the model. Finally, the authors built the output-feedback predictive controller based on the channel-separation principle combined with direct observation of the continuous model for controlling the motion of the underactuated ship in the case of uncertain disturbance and the bound control signals. The result is that the movement quality of the underactuated ship is very good in the context of uncertain disturbance and bound control signals. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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15 pages, 3955 KiB

Open AccessArticle

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

by Andrés Morán-Durán, Albino Martínez-Sibaja, José Pastor Rodríguez-Jarquin, Rubén Posada-Gómez and Oscar Sandoval González

Processes 2019, 7(7), 434; https://doi.org/10.3390/pr7070434 - 10 Jul 2019

Cited by 27 | Viewed by 5035

Abstract

Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial [...] Read more.

Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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14 pages, 873 KiB

Open AccessArticle

Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

by Rashida Adeeb Khanum, Muhammad Asif Jan, Nasser Tairan, Wali Khan Mashwani, Muhammad Sulaiman, Hidayat Ullah Khan and Habib Shah

Processes 2019, 7(6), 362; https://doi.org/10.3390/pr7060362 - 11 Jun 2019

Cited by 6 | Viewed by 3361

Abstract

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local [...] Read more.

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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24 pages, 629 KiB

Open AccessArticle

Rare Event Chance-Constrained Optimal Control Using Polynomial Chaos and Subset Simulation

by Patrick Piprek, Sébastien Gros and Florian Holzapfel

Processes 2019, 7(4), 185; https://doi.org/10.3390/pr7040185 - 30 Mar 2019

Cited by 8 | Viewed by 4530

Abstract

This study develops a chance–constrained open–loop optimal control (CC–OC) framework capable of handling rare event probabilities. Therefore, the framework uses the generalized polynomial chaos (gPC) method to calculate the probability of fulfilling rare event constraints under uncertainties. Here, the resulting chance constraint (CC) [...] Read more.

This study develops a chance–constrained open–loop optimal control (CC–OC) framework capable of handling rare event probabilities. Therefore, the framework uses the generalized polynomial chaos (gPC) method to calculate the probability of fulfilling rare event constraints under uncertainties. Here, the resulting chance constraint (CC) evaluation is based on the efficient sampling provided by the gPC expansion. The subset simulation (SubSim) method is used to estimate the actual probability of the rare event. Additionally, the discontinuous CC is approximated by a differentiable function that is iteratively sharpened using a homotopy strategy. Furthermore, the SubSim problem is also iteratively adapted using another homotopy strategy to improve the convergence of the Newton-type optimization algorithm. The applicability of the framework is shown in case studies regarding battery charging and discharging. The results show that the proposed method is indeed capable of incorporating very general CCs within an open–loop optimal control problem (OCP) at a low computational cost to calculate optimal results with rare failure probability CCs. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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12 pages, 3119 KiB

Open AccessArticle

A Novel Method for Gas Turbine Condition Monitoring Based on KPCA and Analysis of Statistics T² and SPE

by Li Zeng, Wei Long and Yanyan Li

Processes 2019, 7(3), 124; https://doi.org/10.3390/pr7030124 - 27 Feb 2019

Cited by 19 | Viewed by 3825

Abstract

Gas turbines are widely used all over the world, in order to ensure the normal operation of gas turbines, it is necessary to monitor the condition of gas turbine and analyze the tested parameters to find the state information contained in parameters. There [...] Read more.

Gas turbines are widely used all over the world, in order to ensure the normal operation of gas turbines, it is necessary to monitor the condition of gas turbine and analyze the tested parameters to find the state information contained in parameters. There is a problem in gas turbine condition monitoring that how to locate the fault accurately if failure occurs. To solve the problem, this paper proposes a method to locate the fault of gas turbine components by evaluating the sensitivity of tested parameters to fault. Firstly, the tested parameters are decomposed by the kernel principal component analysis. Then construct the statistics of T² and SPE in the principal elements space and residual space, respectively. Furthermore, the thresholds of the statistics must be calculated. The influence of tested parameters on faults is analyzed, and the degree of influence is quantified. The fault location can be realized according to the analysis results. The research results show that the proposed method can realize fault diagnosis and location accurately. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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14 pages, 4922 KiB

Open AccessArticle

Availability Assessment of IMA System Based on Model-Based Safety Analysis Using AltaRica 3.0

by Haiyong Dong, Qingfan Gu, Guoqing Wang, Zhengjun Zhai, Yanhong Lu and Miao Wang

Processes 2019, 7(2), 117; https://doi.org/10.3390/pr7020117 - 25 Feb 2019

Cited by 19 | Viewed by 4908

Abstract

The integrated modular avionics (IMA) system is widely used in all classes of aircraft as a result of its high functional integration and resource utilization in developing advanced avionics systems. However, a series of challenges related to safety assessment exist in the background [...] Read more.

The integrated modular avionics (IMA) system is widely used in all classes of aircraft as a result of its high functional integration and resource utilization in developing advanced avionics systems. However, a series of challenges related to safety assessment exist in the background of the logical architecture for multi-message interactions of the IMA system. Traditional safety assessment methods are mainly based on engineering experience, and are difficult to reuse, incomplete, and even error-prone. Here we propose a method to assess the availability of the IMA system based on the thinking of model-based safety analysis. To aid the proposed method, we implement a tool to generate a AltaRica 3.0 file used to assess the IMA system model. The simulation results show that the proposed method makes the availability assessment fast, efficient, and effective. Moreover, we apply this method to the modification analysis of the IMA system under the condition of satisfying the safety requirement. Our study can enhance the safety assessment of safety-critical systems effectively, assist the design of IMA systems, and reduce the amount of errors during the programming process of the safety model. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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21 pages, 2757 KiB

Open AccessArticle

Profile Monitoring for Autocorrelated Reflow Processes with Small Samples

by Shu-Kai S. Fan, Chih-Hung Jen and Jai-Xhing Lee

Processes 2019, 7(2), 104; https://doi.org/10.3390/pr7020104 - 15 Feb 2019

Cited by 8 | Viewed by 4877

Abstract

The methodology of profile monitoring combines both the model fitting and statistical process control (SPC) techniques. Over the past ten years, a variety of profile monitoring methods have been proposed and extensively investigated in terms of different process profiles. However, monitoring tasks still [...] Read more.

The methodology of profile monitoring combines both the model fitting and statistical process control (SPC) techniques. Over the past ten years, a variety of profile monitoring methods have been proposed and extensively investigated in terms of different process profiles. However, monitoring tasks still exhibit a primary problem in that the errors surrounding the functional relationship are frequently assumed to be independent within every single profile. However, the assumption of independence is an unrealistic assumption in many practical instances. In particular, within-profile autocorrelation often occurs in the profile data. To mitigate the within-profile autocorrelation, a monitoring method incorporating an autoregressive (AR)(1) model to cope with autocorrelation is proposed. In this paper, the reflow process with small samples in surface mount technology (SMT) is investigated. In Phase I, three different process models are compared in combination with the first-order autoregressive model, while an appropriate profile model is sought. The Hotelling T² and exponentially weighted moving average (EWMA) control charts are used together to monitor the parameter estimates (i.e., profile shape) and residuals (i.e., profile variability), respectively. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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19 pages, 6086 KiB

Open AccessArticle

Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries

by Jeongeun Son and Yuncheng Du

Processes 2019, 7(1), 38; https://doi.org/10.3390/pr7010038 - 13 Jan 2019

Cited by 33 | Viewed by 5149

Abstract

The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety [...] Read more.

The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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15 pages, 1835 KiB

Open AccessArticle

On the Boundary Conditions in a Non-Linear Dissipative Observer for Tubular Reactors

by Irandi Gutierrez-Carmona, Jaime A. Moreno and H.F. Abundis-Fong

Processes 2019, 7(1), 8; https://doi.org/10.3390/pr7010008 - 28 Dec 2018

Cited by 2 | Viewed by 2763

Abstract

The modal injection mechanism ensures the exponential convergence of an observer in a continuous tubular reactor in dependence with the system parameters, the sensor location, and the observer gains. In this paper, it is shown that by simple considerations in the boundary conditions, [...] Read more.

The modal injection mechanism ensures the exponential convergence of an observer in a continuous tubular reactor in dependence with the system parameters, the sensor location, and the observer gains. In this paper, it is shown that by simple considerations in the boundary conditions, the observer convergence is improved regardless of the presence of perturbations, the sensor locations acquire a meaningful physical meaning, and by simple numerical manipulations, the perturbations in the inflow can be numerically estimated. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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21 pages, 3797 KiB

Open AccessArticle

FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time

by Aydın Mühürcü

Processes 2019, 7(1), 4; https://doi.org/10.3390/pr7010004 - 21 Dec 2018

Cited by 3 | Viewed by 3929

Abstract

In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. [...] Read more.

In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order physical system (buck converter) that was structured with power electronics elements on Simulink platform. Finally, the success of the control process was discussed by observing results. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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14 pages, 2497 KiB

Open AccessArticle

Effect of Control Horizon in Model Predictive Control for Steam/Water Loop in Large-Scale Ships

by Shiquan Zhao, Anca Maxim, Sheng Liu, Robin De Keyser and Clara Ionescu

Processes 2018, 6(12), 265; https://doi.org/10.3390/pr6120265 - 14 Dec 2018

Cited by 20 | Viewed by 5604

Abstract

This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework. Analysis is performed on the linear multivariable model of the steam/water loop in large-scale watercraft/ships. The results indicate [...] Read more.

This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework. Analysis is performed on the linear multivariable model of the steam/water loop in large-scale watercraft/ships. The results indicate that larger control horizon values lead to better loop performance, at the cost of computational complexity. Hence, it is necessary to find a good trade-off between the performance of the system and allocated or available computational complexity. In this original work, this problem is explicitly treated as an optimization task, leading to the optimal control horizon sets for the steam/water loop example. Based on simulation results, it is concluded that specific tuning of control horizons outperforms the case when only a single valued control horizon is used for all the loops. Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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Review

Jump to: Editorial, Research

39 pages, 1611 KiB

Open AccessFeature PaperReview

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

by Francisco-Ronay López-Estrada, Damiano Rotondo and Guillermo Valencia-Palomo

Processes 2019, 7(11), 814; https://doi.org/10.3390/pr7110814 - 4 Nov 2019

Cited by 53 | Viewed by 5370

Abstract

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated [...] Read more.

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC). Full article

(This article belongs to the Special Issue Optimization for Control, Observation and Safety)

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