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Algorithms
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Algorithms for Reliable Estimation, Identification and Control
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Algorithms for Reliable Estimation, Identification and Control

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 25872

Special Issue Editor

Dr. Andreas Rauh

E-Mail Website
Guest Editor
Chair of Mechatronics, Faculty of Mechanical Engineering and Marine Technology, University of Rostock, Justus-von-Liebig-Weg 6, 18059 Rostock, Germany
Interests: interval analysis; state estimation; stochastic filtering techniques; robust control; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The optimization of feedforward and feedback controllers with respect to predefined performance criteria as well as the enhancement and verification of their robustness with respect to external disturbances and uncertain parameters are widespread aspects of current research activities. The same holds for the reliable estimation of non-measurable system states and for the identification of parameters based on uncertain measurements. Possible applications of related optimization algorithms can be found not only in the frame of a control and estimator synthesis, but also in the field of reliable modeling and in the model-based analysis of measured data.
This Special Issue aims at providing a platform for the publication of novel algorithms in the frame of reliable and optimal estimation and control. Moreover, application-oriented aspects highlighting the practical applicability of theoretical approaches are highly welcome.

Possible topics of interest include:

  • Optimal and robust control of finite-dimensional systems;
  • Optimization and robustness analysis for partial differential equations;
  • Representation of epistemic and aleatory uncertainty by means of:
  • Interval analysis; and
  • Stochastic modeling procedures;
  • Structural optimization of controllers and state observers;
  • Parameter optimization and identification;
  • Stability analysis.

Dr. Andreas Rauh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (6 papers)

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Research

18 pages, 3046 KiB  
Article
Experiments-Based Comparison of Different Power Controllers for a Solid Oxide Fuel Cell Against Model Imperfections and Delay Phenomena
by Wiebke Frenkel, Andreas Rauh, Julia Kersten and Harald Aschemann
Algorithms 2020, 13(4), 76; https://doi.org/10.3390/a13040076 - 25 Mar 2020
Cited by 10 | Viewed by 3357
Abstract
Solid oxide fuel cell systems such as those presented in this paper are not only applicable for a pure supply with electric energy, they can typically also be used in decentralized power stations, i.e., as micro-cogeneration systems for houses, where both electric and [...] Read more.
Solid oxide fuel cell systems such as those presented in this paper are not only applicable for a pure supply with electric energy, they can typically also be used in decentralized power stations, i.e., as micro-cogeneration systems for houses, where both electric and thermal energy are required. For that application, obviously, the electric power need is not constant but rather changes over time. In such a way, it essentially depends on the user profiles of said houses which can refer to e.g., private households as well as offices. The power use is furthermore not predefined. For an optimal operation of the fuel cell, we want to adjust the power, to match the need with sufficiently small time constants without the implementation of mid- or long-term electrical storage systems such as battery buffers. To adapt the produced electric power a simple, however, sufficiently robust feedback controller regulating the hydrogen mass flow into the cells is necessary. To achieve this goal, four different controllers, namely, a PI output-feedback controller combined with a feedforward control, an internal model control (IMC) approach, a sliding-mode (SM) controller and a state-feedback controller, are developed and compared in this paper. As the challenge is to find a controller ensuring steady-state accuracy and good tracking behavior despite the nonlinearities and uncertainties of the plant, the comparison was done regarding these requirements. Simulations and experiments show that the IMC outperforms the alternatives with respect to steady-state accuracy and tracking behavior. Full article
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
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17 pages, 2260 KiB  
Article
Observability of Uncertain Nonlinear Systems Using Interval Analysis
by Thomas Paradowski, Sabine Lerch, Michelle Damaszek, Robert Dehnert and Bernd Tibken
Algorithms 2020, 13(3), 66; https://doi.org/10.3390/a13030066 - 16 Mar 2020
Cited by 10 | Viewed by 4616
Abstract
In the field of control engineering, observability of uncertain nonlinear systems is often neglected and not examined. This is due to the complex analytical calculations required for the verification. Therefore, the aim of this work is to provide an algorithm which numerically analyzes [...] Read more.
In the field of control engineering, observability of uncertain nonlinear systems is often neglected and not examined. This is due to the complex analytical calculations required for the verification. Therefore, the aim of this work is to provide an algorithm which numerically analyzes the observability of nonlinear systems described by finite-dimensional, continuous-time sets of ordinary differential equations. The algorithm is based on definitions for distinguishability and local observability using a rank check from which conditions are deduced. The only requirements are the uncertain model equations of the system. Further, the methodology verifies observability of nonlinear systems on a given state space. In case that the state space is not fully observable, the algorithm provides the observable set of states. In addition, the results obtained by the algorithm allows insight into why the remaining states cannot be distinguished. Full article
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
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17 pages, 2821 KiB  
Article
Kalman Filter-Based Online Identification of the Electric Power Characteristic of Solid Oxide Fuel Cells Aiming at Maximum Power Point Tracking
by Andreas Rauh, Wiebke Frenkel and Julia Kersten
Algorithms 2020, 13(3), 58; https://doi.org/10.3390/a13030058 - 2 Mar 2020
Cited by 12 | Viewed by 3714
Abstract
High-temperature fuel cells are one of the devices currently investigated for an integration into distributed power supply grids. Such distributed grids aim at the simultaneous production of thermal energy and electricity. To maximize the efficiency of fuel cell systems, it is reasonable to [...] Read more.
High-temperature fuel cells are one of the devices currently investigated for an integration into distributed power supply grids. Such distributed grids aim at the simultaneous production of thermal energy and electricity. To maximize the efficiency of fuel cell systems, it is reasonable to track the point of maximum electric power production and to operate the system in close vicinity to this point. However, variations of gas mass flows, especially the concentration of hydrogen contained in the anode gas, as well as variations of the internal temperature distribution in the fuel cell stack module lead to the fact that the maximum power point changes in dependence of the aforementioned phenomena. Therefore, this paper first proposes a real-time capable stochastic filter approach for the local identification of the electric power characteristic of the fuel cell. Second, based on this estimate, a maximum power point tracking procedure is derived. It is based on an iteration procedure under consideration of the estimation accuracy of the stochastic filter and adjusts the fuel cell’s electric current so that optimal operating points are guaranteed. Numerical simulations, based on real measured data gathered at a test rig available at the Chair of Mechatronics at the University of Rostock, Germany, conclude this paper. Full article
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
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13 pages, 493 KiB  
Article
Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
by Mohamed Abdelaal and Steffen Schön
Algorithms 2020, 13(3), 52; https://doi.org/10.3390/a13030052 - 28 Feb 2020
Cited by 9 | Viewed by 4562
Abstract
This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of [...] Read more.
This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bézier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bézier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit. Full article
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
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12 pages, 2558 KiB  
Article
Unstructured Uncertainty Based Modeling and Robust Stability Analysis of Textile-Reinforced Composites with Embedded Shape Memory Alloys
by Najmeh Keshtkar and Klaus Röbenack
Algorithms 2020, 13(1), 24; https://doi.org/10.3390/a13010024 - 15 Jan 2020
Cited by 8 | Viewed by 4376
Abstract
This paper develops the mathematical modeling and deflection control of a textile-reinforced composite integrated with shape memory actuators. The mathematical model of the system is derived using the identification method and an unstructured uncertainty approach. Based on this model and a robust stability [...] Read more.
This paper develops the mathematical modeling and deflection control of a textile-reinforced composite integrated with shape memory actuators. The mathematical model of the system is derived using the identification method and an unstructured uncertainty approach. Based on this model and a robust stability analysis, a robust proportional–integral controller is designed for controlling the deflection of the composite. We showed that the robust controller depends significantly on the modeling of the uncertainty. The performance of the proposed controller is compared with a classical one through experimental analysis. Experimental results show that the proposed controller has a better performance as it reduces the overshoot and provide robustness to uncertainty. Due to the robust design, the controller also has a wide operating range, which is advantageous for practical applications. Full article
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
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20 pages, 1967 KiB  
Article
Using Interval Analysis to Compute the Invariant Set of a Nonlinear Closed-Loop Control System
by Swantje Romig, Luc Jaulin and Andreas Rauh
Algorithms 2019, 12(12), 262; https://doi.org/10.3390/a12120262 - 6 Dec 2019
Cited by 11 | Viewed by 3851
Abstract
In recent years, many applications, as well as theoretical properties of interval analysis have been investigated. Without any claim for completeness, such applications and methodologies range from enclosing the effect of round-off errors in highly accurate numerical computations over simulating guaranteed enclosures of [...] Read more.
In recent years, many applications, as well as theoretical properties of interval analysis have been investigated. Without any claim for completeness, such applications and methodologies range from enclosing the effect of round-off errors in highly accurate numerical computations over simulating guaranteed enclosures of all reachable states of a dynamic system model with bounded uncertainty in parameters and initial conditions, to the solution of global optimization tasks. By exploiting the fundamental enclosure properties of interval analysis, this paper aims at computing invariant sets of nonlinear closed-loop control systems. For that purpose, Lyapunov-like functions and interval analysis are combined in a novel manner. To demonstrate the proposed techniques for enclosing invariant sets, the systems examined in this paper are controlled via sliding mode techniques with subsequently enclosing the invariant sets by an interval based set inversion technique. The applied methods for the control synthesis make use of a suitably chosen Gröbner basis, which is employed to solve Bézout’s identity. Illustrating simulation results conclude this paper to visualize the novel combination of sliding mode control with an interval based computation of invariant sets. Full article
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
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