New Trends in Observer-based Control: A Practical Guide to Process and Engineering Applications

By Magdi S. Mahmoud

New Trends in Observer-Based Control: A Practical Guide to Process and Engineering Applications presents a concise introduction to the latest advances in observer-based control design. The book gives a comprehensive tutorial on new trends in the design of observer-based controllers for which the separation principle is well established. It covers a wide range of applications, also including worked examples that make it ideal for both advanced courses and researchers starting work in the field. This book is also particularly suitable for engineers who want to quickly and efficiently enter the field.

• Presents a clear-and-concise introduction to the latest advances in observer-based control design
• Offers content on many facets of observer-based control design
• Discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems

• Language: English
• Publisher: Academic Press
• Release date: Aug 23, 2019
• ISBN: 9780128170359

Book preview

New Trends in Observer-based Control

A Practical Guide to Process and Engineering Applications, Volume 2

First Edition

Olfa Boubaker

Quanmin Zhu

Magdi S. Mahmoud

José Ragot

Hamid Reza Karimi

Jorge Dávila

Table of Contents

Cover image

Title page

Copyright

Contributors

Preface of Vol. 2, New Trends in Observer-Based Control A Practical Guide to Process and Engineering Applications

Part I: Power, Renewable Energy, and Industrial Systems

Chapter 1: Observer-Based Controller of Analytical Complex Systems: Application for a Large-Scale Power System

Abstract

1 Introduction

2 State Observer-Based Controller of Analytical Nonlinear Systems

3 Decentralized Observer-Based Controller of Interconnected Analytical Nonlinear Systems

4 An Interconnected System Case Study: Stabilization of a Three-Machine Power System Using a Decentralized Observer-Based Controller

5 Conclusion

Appendix Kronecker Product: Mathematical Notations and Properties

Chapter 2: LMI Region-Based Nonlinear Disturbance Observer With Application to Robust Wind Turbine Control

Abstract

1 Introduction

2 Takagi-Sugeno Modeling

3 LMI Control and Observer Synthesis

4 Application to Wind Turbine Control

5 Conclusion

Chapter 3: Stochastic Control Approach for Distributed Generation Units Interacting on Graphs

Abstract

1 Introduction

2 Dynamic Graphical Games

3 Kalman Filter for Dynamic Graphical Games

4 Online Adaptive RL Solution

5 Critic Neural Network Implementation for the Graphical Game’s Solution

6 The Dynamic Model of the Generation Unit

7 The Simulation Outcomes

8 Conclusion

Chapter 4: Control of Anaerobic Digestion Process

Abstract

1 Introduction

2 Mathematical Model of the AD Process

3 State-Feedback Trajectory Tracking via LMIs

4 Observer-Based Reference Trajectory Tracking

5 Simulation Results

6 Conclusion

Part II: Robotics, Flight Systems, and Vehicle Dynamics

Chapter 5: Finite-Time Disturbance Observer-Based Tracking Control Design for Nonholonomic Systems

Abstract

1 Introduction

2 Problem Statement and Preliminaries

3 Disturbance Observer-Based TSMC Approach

4 Simulation Results

5 Conclusion

Chapter 6: Design of a Composite Control in Two-Time Scale Using Nonlinear Disturbance Observer-Based SMC and Backstepping Control of a Two-Link Flexible Manipulator

Abstract

1 Introduction

2 Modeling of the Two-Link FM

3 Singular Perturbation Modeling of a TLFM

4 Design of a Composite Control Using NDO-Based SMC and Backstepping Control

5 Results and Discussion for the Composite Control

6 Conclusion

Chapter 7: Design of Observer-Based Tracking Controller for Robotic Manipulators

Abstract

1 Introduction

2 Mathematical Model of Robotic Manipulators

3 Observer-Based Tracking Controller

4 Simulation Results

5 Conclusion

Chapter 8: Disturbance Observer-Based Control of Spacecraft Attitude Dynamics Subject to Perturbations and Underactuation

Abstract

1 Introduction

2 Problem Formulation

3 Disturbance Observer-Based Magnetic Sliding Mode Attitude Controller

4 Comparison Through Simulations

5 Conclusion

Performance for Fault Diagnosis: Application to the Mars Sample Return Mission

Abstract

1 Introduction

2 The Rendezvous Scenario

3 Modeling Issues

4 Design of the Thruster FDI Unit

5 Simulation Campaign

6 Conclusion

Chapter 10: Actuator and Sensor Fault Detection Based on LPV Unknown Input Observer Applied to Lateral Vehicle Dynamics

Abstract

1 Introduction

2 Vehicle Lateral Dynamics Model

3 Unknown Input Observer and Fault Estimation

4 Simulation Results

5 Conclusion

Index

Copyright

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Contributors

Mohammed Abouheaf     School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

Abdel Aitouche     Automation, HEI Lille and CRIStAL Laboratory, Lille, France

Ibrahim Alaridh     CRIStAL Laboratory, University of Lille, Villeneuve-d’Ascq, France

Marouane Alma     University of Lorraine, Nancy, France

Nezar Alyazidi     Systems Engineering, College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

Finn Ankersen     European Space Agency, DTEC, Noordwijk, The Netherlands

Olfa Boubaker     University of Carthage, National Institute of Applied Sciences and Technology, Tunis, Tunisia

Naceur Benhadj Braiek     Advanced Systems Laboratory, Polytechnic School of Tunisia, La Marsa, Tunisia

Mohamed Darouach     University of Lorraine, Nancy, France

Salwa Elloumi     Advanced Systems Laboratory, Polytechnic School of Tunisia, La Marsa, Tunisia

Afef Fekih     Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA, United States

Eckhard Gauterin     Department of Engineering I, Control Engineering, HTW Berlin, Berlin, Germany

David Henry     IMS Laboratory, University of Bordeaux, Talence, France

Kshetrimayum Lochan

Department of Electrical Engineering, National Institute of Technology Silchar, Silchar

Department of Mechatronics, Manipal Institute of Technology, Manipal, India

Magdi S. Mahmoud     Systems Engineering, College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

Saleh Mobayen     Advanced Control Systems Laboratory, Department of Electrical Engineering, University of Zanjan, Zanjan, Iran

Florian Pöschke     Department of Engineering I, Control Engineering, HTW Berlin, Berlin, Germany

Binoy Krishna Roy     Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India

Horst Schulte     Department of Engineering I, Control Engineering, HTW Berlin, Berlin, Germany

Adel Sharaf     SHARAF Energy Systems, Inc., Fredericton, NB, Canada

Jay Prakash Singh     Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India

Ahmet Sofyali     Independent Researcher, Istanbul, Turkey

Luigi Strippoli     GMV Aerospace and Defence S.A.U., Madrid, Spain

Bidyadhar Subudhi     Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela, India

Holger Voos     Faculty of Science, Technology and Communication, University of Luxembourg, Luxembourg City, Luxembourg

Ali Zemouche     University of Lorraine, Nancy, France

Quan Min Zhu     Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

Khadidja Chaib Draa     Faculty of Science, Technology and Communication, University of Luxembourg, Luxembourg City, Luxembourg

Preface of Vol. 2, New Trends in Observer-Based Control A Practical Guide to Process and Engineering Applications

Magdi S. Mahmoud, Saudi Arabia

In recent decades, it has been widely recognized that one of the challenging problems in modern control theory deals with observer design for dynamical systems where tremendous research activities have been developed covering different aspects.

This edited book, New Trends in Observer-Based Control A Practical Guide to Process and Engineering Applications, aims to present a full picture of the state-of-the-art research and development of analysis techniques and design methodologies pertaining to observer-based control and its wide applications. The ultimate purpose is to provide a coherent dose of valuable information to researchers, professional engineers, graduate students, and interested readers. Particular attention is given to properly orient the various chapters so that the integrated book stands as a key reference in academia and international libraries.

The book is triggered by ubiquitous applications of observer-based control (OBC for short), and the recurrent development of efficient algorithms for various types of dynamical systems. OBC plays an important role in modern process control infrastructures.

The book contains two main volumes:

1.Vol. 1 covers Chapters 1 through 13, subdivided into three parts (Parts I–III).

2.Vol. 2 includes Chapters 1 through 10, subdivided into two parts (Parts I and II).

This is the preface of Vol. 2, in which Part I deals with topics on Power, Renewable Energy, and Industrial Systems and Part II deals with Robotics, Flight Systems, and Vehicle Dynamics.

We start with Part I where in Chapter 1, titled Observer-Based Controller of Analytical Complex Systems: Application for a Large-Scale Power System, the nonlinear control with a state observer of complex systems modeled in polynomial form is addressed. The basic idea of the chapter is to design a nonlinear control law with a state observer, guaranteeing the overall asymptotic stability of the augmented system formed by the nonlinear process, the control, and the observer. The design of the observer-based control is formulated using the Kronecker product and the power of matrices properties for the state-space description of polynomial systems. The stability of the observer-based polynomial system augmented is analyzed using the direct method of Lyapunov. Further extension was achieved for the case of several interconnected subsystems using a decentralized control law accompanied by a decentralized state observer. Simulation studies on three machine power systems demonstrate the potential performances of the design approach.

Then in Chapter 2, titled LMI Region-Based Nonlinear Disturbance Observer With Application to Robust Wind Turbine Control, the authors investigate the Takagi-Sugeno (T-S) modeling approach for nonlinear control and observer design. A linear matrix inequality (LMI) constraint for T-S observers with unmeasurable premise variables based on the input-to-state property is established. By combining the presented LMIs for the application to a wind turbine, two different nonlinear control schemes based on the convex system description are derived, and the implications from introducing the observer for the wind turbine application are discussed.

Next, Chapter 3, titled Stochastic Control Approach for Distributed Generation Units Interacting on Graphs, provides a novel online adaptive learning distributed control approach for a system of distributed generation units with disturbances in their dynamical environments. The interactions between the generation units are restricted by a graph topology to reflect intercoupling of the dynamics of the generation units. Distributed protocols are utilized to maintain synchronization among the generation units. In this case, the cost function is designed to take into account the neighborhood interactions and the graph topology. A distributed online reinforcement learning approach that employs a Kalman filter is employed to solve the optimal control problem of the multiagent system and is implemented in real time using a means of neural network approximations. The validity of the distributed control approach is tested using a system of distributed generation units working under disturbances.

Chapter 4, titled Control of Anaerobic Digestion Process, deals with the LMI design of observer-based control strategies for the anaerobic digestion (AD) process. The AD process is represented by a mass balance nonlinear model where the key variables are not accessible for measurement. Therefore, the control scheme is composed by a nonlinear state observer and a feedback control. The objective is to track state trajectories reflecting desired biogas production. Stability analysis is performed by using the Lipschitz conditions and the Lyapunov function, leading to LMI-based stability conditions.

Now moving to Part II, we begin with Chapter 5, titled Finite-Time Disturbance Observer-Based Tracking Control Design for Nonholonomic Systems, in which the authors examine a disturbance observer-based terminal sliding mode control (TSMC) approach for a class of nonholonomic systems subject to unknown perturbations. In this way, the approach integrates the robust stability properties of the sliding mode control with the compensation capacity of nonlinear disturbance observers (DOBs) and guarantees convergence behavior in finite time. The design performance is assessed using a wheeled mobile robot.

In Chapter 6, titled Design of a Composite Control in Two-Time Scale Using Nonlinear Disturbance Observer-Based SMC and Backstepping Control of a Two-Link Flexible Manipulator, the authors investigate the problem of trajectory tracking control in the presence of unmatched disturbances and quick tip deflection suppression for a two-link flexible manipulator is considered. The solution approach is based on designing a composite (slow-fast) control technique in which the slow subsystem consists of the rigid body dynamics of the flexible manipulator and the fast subsystem consists of the flexible body dynamics of the flexible manipulator. A nonlinear disturbance observer-based SMC is designed on the slow subsystem under the presence of unmatched disturbances for the trajectory tracking control. A backstepping controller in used on the fast subsystem for the quick suppression of the links deflection. A composite controller is finally designed for the two-link flexible manipulator using the nonlinear disturbance observer-based SMC on the slow subsystem and the backstepping controller on the fast subsystem. Simulation results validate the effectiveness of the proposed composite.

Next, in Chapter 7, titled Design of Observer-Based Tracking Controller for Robotic Manipulators, an observer-based tracking control design for an n-link robotic manipulator subject to external disturbances is described. Design of the tracking controller is based on the global sliding mode approach, which eliminates the need for the reaching phase by introducing an extra term in the manifold. The disturbance observer is designed to estimate system disturbances without requiring any knowledge about their upper bounds. The stability analysis of the proposed observer was carried out using Lyapunov stability theory. The performance of the proposed approach was assessed using a three-degrees-of-freedom rigid manipulator. Good tracking performance, robustness to disturbances, and eliminating the need for the reaching phase are among the positive features of the proposed approach.

Chapter 8, titled Disturbance Observer-Based Control of Spacecraft Attitude Dynamics Subject to Perturbations and Underactuation, deals with the stabilization of nonlinear spacecraft attitude dynamics by using solely magnetic actuation. The considered control problem possesses instantaneous underactuation due to the magnetic torque production mechanism. The objective is to design an observer-based control system by integrating an already existing magnetic sliding mode attitude controller with a nonlinear disturbance observer where all major environmental disturbances and complete inertia matrix uncertainty are taken into account. It is proven that the obtained controller realizes sliding mode in the system, which implies robustness of the stabilization. Comparative simulation results indicate the superiority of the ultimate controller to the baseline controller in terms of control effort and chattering while the same state responses are output by both control systems.

Following is filter for robust fault detection and a bank of a new class of unknown input observers for fault isolation. A simulation campaign, based on a nonlinear high-fidelity simulator developed by GMV Space Industries, is conducted within the forced translation phase of the rendezvous phase of the MSR mission, under highly realistic conditions.

Finally, in Chapter 10, titled Actuator and Sensor Fault Detection Based on LPV Unknown Input Observer Applied to Lateral Vehicle Dynamics, the estimation and the detection of actuator and sensor faults via the unknown input observer (UIO) applied to a lateral dynamics model of an automated steering vehicle are presented. The vehicle lateral dynamics have been described by an LPV model, taking into account the variations of the longitudinal velocity. The sensor faults are transformed into an augmented system with actuator faults and a first-order filter is needed to attenuate this noise. The work deals within the estimation of the actuator and sensor faults by converting sensor faults into actuator faults and designing an UIO to estimate both states and faults. The gains of the observer can be calculated by solving LMIs and the convergence of the observer is analyzed. In simulation, a vehicle lateral dynamics model with steering angle actuator fault and yaw velocity sensor fault has been tested and the ensuing results are presented to demonstrate the effectiveness of the proposed approach.

Last but not least, the two volumes of the book would serve as an integrated and invaluable contemporary reference on observer-based control analysis and design. For this purpose, special thanks go to all of my colleagues (editors, authors, reviewers) for their dedicated work and remarkable effort in making this project a great success.

Part I

Power, Renewable Energy, and Industrial Systems

Chapter 1

Observer-Based Controller of Analytical Complex Systems: Application for a Large-Scale Power System

Salwa Elloumi; Naceur Benhadj Braiek    Advanced Systems Laboratory, Polytechnic School of Tunisia, La Marsa, Tunisia

Abstract

This chapter is devoted to nonlinear control with the state observer of complex systems modeled in polynomial form. This kind of system presents the advantage of having the capacity of representing an important class of nonlinear systems and describing accurately the dynamic behavior of many physical processes. The basic idea is to design a nonlinear control law with a state observer, guaranteeing the overall asymptotic stability of the augmented system formed by the nonlinear process, the control, and the observer. The design of the observer-based control leverages some notations from the Kronecker product and the power of matrices properties for the state-space description of polynomial systems. The stability study of the polynomial controlled system augmented by its observer is based on the Lyapunov stability direct method.

We extend our study to the case of large-scale complex systems formed by the interconnection of several subsystems. In this sense, we develop a decentralized control law with decentralized state observer, guaranteeing the asymptotic stability of the augmented interconnected system. The performances of this approach are illustrated on a three-machine power system. Simulation results prove clearly the effectiveness not only of the proposed control, which allows rapid stabilization of the multimachine power system, but also of its associated state observer presenting a fast convergence to the real system states.

Keywords

Complex system; Decentralized control; Observer-based controller; Polynomial system; Power system

1 Introduction

In this chapter, we investigate observer-based controllers of interconnected large-scale systems. These complex systems constitute an important class of nonlinear systems that can describe the dynamical behavior of several physical processes, such as electrical power systems, industrial manipulators, and computer networks, to name a few. However, complex systems are often modeled by the interconnection of several subsystems, which makes it difficult to transfer information for process control. Therefore, possible control strategies are generally based on a decentralized solution because they use only local subsystem information, which reduces complexity and allow the control implementation to be more feasible [1].

Decentralized control of interconnected systems requires complete measurement of the state vector of each subsystem, which is not always valid. It is therefore necessary to use decentralized state observation techniques to reconstruct the nonmeasurable states. However, the synthesis of a separated decentralized control from that of a state observer that is itself decentralized is not obvious because the separation principle is not applicable in this situation. It is then necessary to simultaneously consider the problem of decentralized control and state observation to ensure global asymptotic stabilization.

The basic idea developed in this chapter deals mainly with the synthesis of nonlinear observer-based controller laws for complex systems (whether it is the general case of nonlinear systems or the particular case of interconnected systems), which are modeled in a polynomial form, guaranteeing the asymptotic stability of the augmented system composed of the nonlinear process, the control law, and the state observer.

Our particular interest for polynomial modeling is justified by the fact that the polynomial systems allow the approximation of all the nonlinear analytical systems because the analytic functions can be approached by the Taylor series polynomial development. In addition, polynomial systems present the advantage of having the capacity of representing an important class of nonlinear systems and describing accurately the dynamic behavior of many physical processes such as electrical machines, power systems, manipulator robots, etc. The description of the polynomial system is based on the use of the Kronecker power of the state vector [2].

The study of polynomial systems has been developed in previous works [3–9], and shortly before in the work of Rotella [10] for the modeling, analysis, and synthesis of the feedback control as well as for the state observation. It is worth mentioning that according to our knowledge, there is still no work on the development of state observers for polynomial interconnected systems.

Based on these considerations, this chapter aims to develop a new control approach with state observer nonlinear polynomial systems. This control law, where control and state observation gains are determined by a linear matrix inequality (LMI) resolution, must guarantee the stabilization of the overall augmented system (process + observer + control) by using the Lyapunov direct method for the study of stability, and this while verifying sufficient conditions of stability. The proposed approach is then extended to the case of large-scale interconnected systems.

The elaborated developments are reported in this three-part chapter. The first part is interested in the theoretical basis to design a polynomial observer-based control for nonlinear systems and to determine sufficient LMI global stabilization conditions of the polynomial controlled system augmented by its observer. In the second part, we propose to enlarge the scope of our approach to cover analytical, interconnected, and large-scale systems: we develop a decentralized control law with a decentralized state observer, which guarantees the asymptotic stability of the interconnected system together with its associated control and decentralized state observer. The last part is devoted to an interesting industrial case study of an interconnected large-scale system: an electrical power system composed of three interconnected machines. Our goal is to apply the approach of the second part in order to define a decentralized observer-based control that stabilizes the overall power system, and to prove the applicability and effectiveness of such control.

2 State Observer-Based Controller of Analytical Nonlinear Systems

Observer-based control approaches have been widely studied in the literature. Nevertheless, without a common method to analyze or synthesize general nonlinear systems, only some special and limited classes of nonlinear systems have been considered up to now [11–17]. Obviously, many research activities are still in progress looking for new approaches to study and design valid and effective controls for nonlinear systems [18, 19].

This part focuses on the design of a new control approach with a state observer of analytical nonlinear systems