Special Issue "Algorithms for PID Controller 2019"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 December 2019).

Special Issue Editor

Prof. Anastasios Dounis
E-Mail Website
Guest Editor
Faculty of Engineering, University of West Attica, 12243 Athens, Greece
Interests: Computational Intelligence and Evolutionary computation, Fuzzy systems, Fuzzy control and modelling, Fuzzy cognitive maps and Petri nets in decision support systems, Intelligent control, Time series prediction, Automation systems in renewable energy resources, Intelligent energy management systems and smart buildings, Design and management of autonomous smart micro grids, Power electronics in photovoltaic systems, Control electrochromic devices, Modelling and control of reverse osmosis desalination
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Special Issue Information

Dear Colleagues,

The conventional PID (Proportional–Integral–Derivative) controllers are most widely used in industrial applications because of their simple, robust, cheap, and good performances. To date, the PID control performance remains limited. The requirements for control precision have become higher, and the real systems have become more complex, including higher order, time-delayed linear, nonlinear systems, and systems without a mathematical model and uncertainties. The goal of control algorithms is to determine the optimal PID controller parameters. Practically, all PID controllers made today are based on microprocessors. This has created opportunities to provide additional features, such as automatic tuning, gain scheduling, and continuous adaptation. In addition to the conventional approaches such as the Lyapunov approach and PID control system analysis, there are more advanced and intelligent algorithms for PID tuning methods and metaheuristic algorithms, such as the Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Big Bang–Big Crunch, etc. In addition, sophisticated control strategies, such as predictive control, self-tuning methods, fuzzy and neural algorithms, are designed to overcome the problems associated with the regulation of PID controller gains.

Prof. Dr. Anastasios Dounis
Guest Editor

Manuscript Submission Information

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Keywords

  • Evolutionary PID control
  • Adaptive fuzzy PID control
  • Robust PID algorithms
  • Uncertainty of PID algorithm
  • Predictive control
  • Interval type-2 fuzzy PID controller
  • Reinforcement learning algorithm
  • Sliding mode
  • Lyapunov approach
  • Kalman filtering
  • Implementations

Published Papers (6 papers)

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Research

Open AccessArticle
PD Steering Controller Utilizing the Predicted Position on Track for Autonomous Vehicles Driven on Slippery Roads
Algorithms 2020, 13(2), 48; https://doi.org/10.3390/a13020048 - 21 Feb 2020
Abstract
Among the most important characteristics of autonomous vehicles are the safety and robustness in various traffic situations and road conditions. In this paper, we focus on the development and analysis of the extended version of the canonical proportional-derivative PD controllers that are known [...] Read more.
Among the most important characteristics of autonomous vehicles are the safety and robustness in various traffic situations and road conditions. In this paper, we focus on the development and analysis of the extended version of the canonical proportional-derivative PD controllers that are known to provide a good quality of steering on non-slippery (dry) roads. However, on slippery roads, due to the poor yaw controllability of the vehicle (suffering from understeering and oversteering), the quality of control of such controllers deteriorates. The proposed predicted PD controller (PPD controller) overcomes the main drawback of PD controllers, namely, the reactiveness of their steering behavior. The latter implies that steering output is a direct result of the currently perceived lateral- and angular deviation of the vehicle from its intended, ideal trajectory, which is the center of the lane. This reactiveness, combined with the tardiness of the yaw control of the vehicle on slippery roads, results in a significant lag in the control loop that could not be compensated completely by the predictive (derivative) component of these controllers. In our approach, keeping the controller efforts at the same level as in PD controllers by avoiding (i) complex computations and (ii) adding additional variables, the PPD controller shows better quality of steering than that of the evolved (via genetic programming) models. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2019)
Open AccessArticle
Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter
Algorithms 2020, 13(2), 40; https://doi.org/10.3390/a13020040 - 18 Feb 2020
Abstract
Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, [...] Read more.
Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, traditional linear controllers have limitations when controlling highly nonlinear and uncertain systems such as UAVs. In this paper, a control scheme for the pose of a quadrotor is presented. The scheme presented has the behavior of a PD controller and it is based on a Multilayer Perceptron trained with an Extended Kalman Filter. The Neural Network is trained online in order to ensure adaptation to changes in the presence of dynamics and uncertainties. The control scheme is tested in real time experiments in order to show its effectiveness. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2019)
Open AccessArticle
Multi-Loop Model Reference Proportional Integral Derivative Controls: Design and Performance Evaluations
Algorithms 2020, 13(2), 38; https://doi.org/10.3390/a13020038 - 13 Feb 2020
Abstract
Due to unpredictable and fluctuating conditions in real-world control system applications, disturbance rejection is a substantial factor in robust control performance. The inherent disturbance rejection capacity of classical closed loop control systems is limited, and an increase in disturbance rejection performance of single-loop [...] Read more.
Due to unpredictable and fluctuating conditions in real-world control system applications, disturbance rejection is a substantial factor in robust control performance. The inherent disturbance rejection capacity of classical closed loop control systems is limited, and an increase in disturbance rejection performance of single-loop control systems affects the set-point control performance. Multi-loop control structures, which involve model reference control loops, can enhance the inherent disturbance rejection capacity of classical control loops without degrading set-point control performance; while the classical closed Proportional Integral Derivative (PID) control loop deals with stability and set-point control, the additional model reference control loop performs disturbance rejection control. This adaptive disturbance rejection, which does not influence set-point control performance, is achieved by selecting reference models as transfer functions of real control systems. This study investigates six types of multi-loop model reference (ML-MR) control structures for PID control loops and presents straightforward design schemes to enhance the disturbance rejection control performance of existing PID control loops. For this purpose, linear and non-linear ML-MR control structures are introduced, and their control performance improvements and certain inherent drawbacks of these structures are discussed. Design examples demonstrate the benefits of the ML-MR control structures for disturbance rejection performance improvement of PID control loops without severely deteriorating their set-point performance. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2019)
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Open AccessArticle
Optimal Learning and Self-Awareness Versus PDI
Algorithms 2020, 13(1), 23; https://doi.org/10.3390/a13010023 - 11 Jan 2020
Abstract
This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be [...] Read more.
This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be re-parameterized as ideal nonlinear feedforward and feedback evaluated within a Simulink simulation. Comparison is made to a custom proportional, derivative, integral controller (modified versions of classical proportional-integral-derivative control) implemented as a feedback control with a specific term to account for the nonlinear coupled motion. Consistent proportional, derivative, and integral gains were used throughout the duration of the experiments. The simulation results will show that akin feedforward control, deterministic self-awareness statements lack an error correction mechanism, relying on learning (which stands in place of feedback control), and the proposed combination of optimal self-awareness statements and a newly demonstrated analytically optimal learning yielded the highest accuracy with the lowest execution time. This highlights the potential effectiveness of a learning control system. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2019)
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Open AccessArticle
Learning Output Reference Model Tracking for Higher-Order Nonlinear Systems with Unknown Dynamics
Algorithms 2019, 12(6), 121; https://doi.org/10.3390/a12060121 - 12 Jun 2019
Cited by 1
Abstract
This work suggests a solution for the output reference model (ORM) tracking control problem, based on approximate dynamic programming. General nonlinear systems are included in a control system (CS) and subjected to state feedback. By linear ORM selection, indirect CS feedback linearization is [...] Read more.
This work suggests a solution for the output reference model (ORM) tracking control problem, based on approximate dynamic programming. General nonlinear systems are included in a control system (CS) and subjected to state feedback. By linear ORM selection, indirect CS feedback linearization is obtained, leading to favorable linear behavior of the CS. The Value Iteration (VI) algorithm ensures model-free nonlinear state feedback controller learning, without relying on the process dynamics. From linear to nonlinear parameterizations, a reliable approximate VI implementation in continuous state-action spaces depends on several key parameters such as problem dimension, exploration of the state-action space, the state-transitions dataset size, and a suitable selection of the function approximators. Herein, we find that, given a transition sample dataset and a general linear parameterization of the Q-function, the ORM tracking performance obtained with an approximate VI scheme can reach the performance level of a more general implementation using neural networks (NNs). Although the NN-based implementation takes more time to learn due to its higher complexity (more parameters), it is less sensitive to exploration settings, number of transition samples, and to the selected hyper-parameters, hence it is recommending as the de facto practical implementation. Contributions of this work include the following: VI convergence is guaranteed under general function approximators; a case study for a low-order linear system in order to generalize the more complex ORM tracking validation on a real-world nonlinear multivariable aerodynamic process; comparisons with an offline deep deterministic policy gradient solution; implementation details and further discussions on the obtained results. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2019)
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Open AccessArticle
Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
Algorithms 2019, 12(4), 86; https://doi.org/10.3390/a12040086 - 24 Apr 2019
Cited by 2
Abstract
This paper presents a robust and precise tension control method for a roll-to-roll (R2R) system. In R2R processing, robust and precise tension control is very important because improper web tension control leads to deterioration in the quality of web material. However, tension control [...] Read more.
This paper presents a robust and precise tension control method for a roll-to-roll (R2R) system. In R2R processing, robust and precise tension control is very important because improper web tension control leads to deterioration in the quality of web material. However, tension control is not easy because the R2R system has a model variation in which the inertia of the web in roll form is changed and external disturbances caused by web slip and crumpled web. Therefore, a disturbance observer (DOB) was proposed to achieve robustness against model variations and external disturbances. DOB is a robust control method widely used in various fields because of its simple structure and excellent performance. Moreover, the web passes through various process steps to achieve the finished product in the R2R process. Particularly, it is important to track the tension when magnitude of the tension varies during process. Feedforward (FF) controller was applied to minimize the tracking error in the transient section where tension changes. Moreover, the signal processing of a sensor using the Kalman filter (KF) in the R2R system greatly improved control performance. Finally, the effectiveness of the proposed control scheme is discussed using experimental results. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2019)
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