Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator

With the wide application of intelligent manufacturing and the development of diversified functions of industrial manipulator, the requirements for the control accuracy and stability of the manipulator servo system are also increasing. The control of industrial manipulator is a time-varying system with nonlinear and strong coupling, which is often affected by uncertain factors, including parameter changing, environmental interference, joint friction and so on. Aiming at the problem of the poor control accuracy of the manipulator. Under the complex disturbance environment, control accuracy of the manipulator will be greatly affected, so this paper proposes an adaptive type-2 fuzzy sliding mode control (AT2FSMC) method applied to the servo control of the industrial manipulator, which realizes the adaptive adjustment of the boundary layer thickness to suppress the trajectory error caused by the external disturbance and weakens the chattering problem of the sliding mode control. The simulation results on a two-axis manipulator indicate that, with the presence of external disturbances, the proposed control method can help the manipulator maintain control signal stability and improve tracking accuracy. It also suppressed chattering produced by sliding mode control (SMC) and strengthening the robustness of the system. Compared with other conventional trajectory tracking control methods, the effectiveness of the proposed method can be reflected. Finally, the proposed method is tested in an actual manipulator to complete a practical trajectory to prove its feasibility

Gain-scheduled sliding-mode-type iterative learning control design for mechanical systems

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

Design and implementation of a soft computing-based controller for a complex mechanical system

Soft-Computing basierende Regler beinhalten Algorithmen, die im Bereich des Maschinellen Lernens einzuordnen sind. Diese Regler sind in der Lage eine geeignete Steuerungsstrategie durch direkte Interaktion mit einer dynamischen Regelstrecke zu entwerfen. Sowohl klassische als auch moderne Reglerentwurfsmethoden hangen von der Genauigkeit des verwendeten dynamischen Systemmodells ab, was insbesondere bei steigender Komplexitat des Systems und auftretenden Modellunsicherheiten nicht mehr uneingeschrankt gewahrleistet werden kann. Die Ziele von Soft- Computing basierenden Reglern sind die Verbesserung der Gute des Regelverhaltens und eine geeignete Anpassung der Regler ohne eine mathematische Modellbildung auf Grundlage von physikalischen Gesetzen. Im Rahmen dieser Arbeit werden funf Algorithmen zur Modellbildung und Regelung dynamischer Systeme untersucht, welche auf dem Mehrschichten-Perzeptron-Netzwerk (Multi-Layer Perceptron network, MLP), auf der Methode der Support Vector Machine (SVM), der Gau-Prozesse, der radialen Basisfunktionen (Radial Basis Functions, RBF) sowie der Fuzzy-Inferenz-Systeme basieren. Im Anschluss an die Darstellung der zugrunde liegenden mathematischen Zusammenhange dieser Methoden sowie deren Hauptanwendungsfelder im Bereich der Modellbildung und Regelung dynamischer Systeme wird eine systematische Evaluierung der funf Methoden diskutiert. Anhand der Verwendung quantitativer Gutekennziern werden diese Methoden fur die Verwendung in der Modellbildung und Regelung dynamischer Systeme vergleichbar gegenubergestellt. Basierend auf den Ergebnissen der Evaluierung wird der SVM-basierte Algorithmus als Kernalgorithmus des Soft-Computing basierenden Reglers verwendet. Der vorgestellte Regler besteht aus zwei Hauptteilen, wobei der erste Teil aus einer Modellfunktion der dynamischen Regelstrecke und einem SVM-basierten Beobachter besteht, und der zweite Teil basierend auf dem Systemmodell eine geeignete Regelstrategie generiert. Die Verikation des SVM-basierten Regleralgorithmus erfolgt anhand eines FEM-Modells eines dynamischen elastischen Balken bzw. einseitig eingespannten elastischen Balkens. Dieses Modell kann z. B. als Ersatzmodell fur das mechanische Verhalten eines exiblen Roboterarms oder einer Flugzeugtrag ache verwendet werden. Der Hauptteil der Modellfunktion besteht aus einem automatischen Systemidentikationsalgorithmus, der auch die Integration eines systematischen Modellbildungsansatzes fur dynamische Systeme ermoglicht.Die Ergebnisse des SVM-basierten Beobachter zeigen ahnliches Verhalten zum Kalman- Bucy Beobachter. Auch die Sensitivitatsanalyse der Parameter zeigt eine bessere Gute der SVM-basierten Beobachter im Vergleich mit den Kalman-Bucy Beobachtern. Im Anschluss wird der SVM-basierte Regler zur Schwingungsregelung des Kragtragers verwendet. Hierbei werden vergleichbare Ergebnisse zum LQR-Regler erzielt. Eine experimentelle Validierung des SVM basierten Reglers erfolgt an Versuchsst anden eines elastischen Biegebalkens sowie eines invertierten Biegebalkens. Die Zustandsbeobachtung fuhrt zu vergleichbaren Ergebnissen verglichen mit einem Kalman-Bucy Beobachter. Auch die Modellbildung des elastischen Balkens fuhrt zu guten Ubereinstimmungen. Die Regelgute des Soft-Computing basierenden Reglers wurde am Versuchsstand des invertierten Biegebalkens experimentell erprobt. Es wird deutlich, dass Ergebnisse im Rahmen der erforderlichen Vorgaben erzielt werden konnen.The focus of this thesis is to obtain a soft computing-based controller for complex mechanical system. soft computing based controllers are based on machine learning algorithm that able to develop suitable control strategies by direct interaction with targeted dynamic systems. Classical and modern control design methods depend on the accuracy of the system dynamic model which cannot be achieved due to the dynamic system complexity and modeling uncertainties. A soft computing-based controller aims to improve the performance of the close loop system and to give the controller adaptation ability as well as to reduce the need for mathematical modeling based on physical laws. In this work ve dierent softcomputing algorithms used in the eld of modeling and controlling dynamic systems are investigated.These algorithms are Multi-Layer Perceptron(MLP) network, Support Vector Machine (SVM),Gaussian process, Radial Basis Function (RBF), and Fuzzy Inference System (FIS). The basic mathematical description of each algorithm is given. Additionally, the most recent applications in modeling and controlling of dynamic system are summarized. A systematic evaluation of the ve algorithms is proposed. The goal of the evaluation is to provide quantitative measure of the performance of soft computing algorithms when used in modeling and controlling a dynamic system. Based on the evaluation, the SVM algorithm is selected as the core learning algorithm for the soft computing based controller. The controller has two main units. The rst unit has two functions of modeling dynamic system and obtaining a SVM-based observer. The second unit is in charge of generating suitable control strategy based on the dynamic model obtained. The verication of the controller using SVM algorithm is done using an elastic cantilever beam modeled using Finite Element Method (FEM). An elastic cantilever beam can be considered as a representation of exible single-link manipulator or aircraft wing. In the core of the modeling unit, an automatic system identication algorithm which allows a systematic modeling approach of dynamic systems is implemented. The results show that the system dynamic model using SVM algorithm is accurate with respect to the FEM model. As for the SVM-based observer the results show that it has good estimation in comparison with to dierent Kalman-Bucy observers. The sensitivity to parameters variations analysis shows that the SVM-based observer has better performance than Kalman-Bucy observer. The SVM based controller is used to control the vibration of the cantilever beam; the results show that the model reference controller using SVM has a similar performance to LQR controller. The validation of the controller using SVM algorithm is carried out using the elastic cantilever beam test rig and the inverted cantilever beam test rig. The states estimation using SVM-based observer of the elastic cantilever beam test rig is successful and accurate compared to a Kalman-Bucy observer. Modeling of the elastic cantilever beam using the SVM algorithm shows good accuracy. The performance of controller is tested on the inverted cantilever beam test rig. The results show that required performance objective can be realized using this control strategy

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.EThOS - Electronic Theses Online ServiceBiruni Remote Sensing Centre, LibyaGBUnited Kingdo