Linear Parameter-Varying Control

Olivier Sename, PhD, is a full Professor at Grenoble INP. His main research focus is on Linear Parameter-Varying systems with automotive applications. He has authored or co-authored four books, around 100 international journal papers, more than 280 international conference papers and 6 patents.

• About the Author xvPreface xviiAcronyms xxiAbout the Companion Website xxiiiIntroduction xxvPart I Some Theoretical Aspects on LPV Systems: From Modeling to Control 11 Some Modeling Approaches for LPV and qLPV Systems 31.1 Introduction 31.2 Dynamical Systems 41.3 An Introduction to LPV Models 51.4 Specific Classes of LPV Systems 101.5 From a Nonlinear Model to an LPV Representation 191.6 An Introduction to Identification of LPV Systems 231.7 The Nonuniqueness Issue: A Control-Oriented LPV Modeling Perspective 251.8 Illustrative Example 1: A Single Tank System 261.9 Illustrative Example 2: qLPV Modeling and Time-Varying Characteristics 301.10 Conclusion 34Bibliography 342 Properties of LPV Systems 412.1 Introduction 412.2 Controllability 422.3 Observability 462.4 Comments on State-Space Realizations of LPV Systems 492.5 Stability 502.6 Performance Criteria: H∞, gH2, and Pole Placement 552.7 About Stabilizability and Detectability 622.8 The Case of Discrete-Time LPV Systems 632.9 Conclusion 68Bibliography 683 Control of LPV Systems 753.1 Introduction 753.2 LPV State-Feedback Control 773.3 The LPV Dynamic Output Feedback Control 883.4 LPV Observer Design 1043.5 About Control of Discrete-Time LPV Systems 1093.6 Conclusion 111Bibliography 111Part II LPV Methods for Nonlinear Systems 1214 Control and Observer Design for Nonlinear Systems Using Quasi-LPV Models: An Illustration Through Examples 1234.1 Introduction 1234.2 H∞∕LPV Control of a Nonlinear System 1244.3 An H∞∕LPV Observer of a Three-Tank Nonlinear System 1344.4 Conclusion 140Bibliography 1405 Observer Design for Semi-active Suspension Systems: qLPV Approaches 1435.1 Introduction 1435.2 Illustrative Case Study: The INOVE Testbench, a Semi-active Suspension System 1455.3 Electro-Rheological Dampers: Modeling Approaches 1475.4 qLPV Quarter Car Semi-active Suspension Models 1525.5 Method 1: An H∞/gH2 Observer for Suspension State Estimation 1585.6 Method 2: A H∞ Filtering Approach for Damper Force Estimation 1635.7 Method 3: A Nonlinear Parameter Varying Approach for State Estimation 1685.8 Concluding Remarks 175Bibliography 1766 Lateral Control of Autonomous Vehicle 1816.1 Introduction 1816.2 Modeling 1826.3 H∞∕LPV Control Design 1876.4 Analysis of the Polytopic and Grid-Based Design Methods 1916.5 Simulation Results 1926.6 Conclusion 197Bibliography 197Part III LPV Adaptive-Like Control Methods 2037 Methods and Tools for LPV Adaptive-Like Control 2057.1 Introduction 2057.2 The H∞Framework: A Generic Tool for “Adaptive-Like” Control 2067.3 LPV Adaptive Control with Varying Closed-Loop Performances (Function of External Parameters) 2087.4 LPV Adaptive Control Function of Varying Endogeneous Parameters 2157.5 Concluding Remarks 223Bibliography 2238 LPV Road Adaptive Suspension Control 2278.1 Introduction 2278.2 The Semi-active Suspension Quarter-Car Model 2308.3 Road Roughness Estimator 2338.4 Synthesis of a Semi-active Suspension Control 2378.5 Simulation Results 2468.6 Conclusions 249Bibliography 2499 LPV Fault-Tolerant Control Strategies for Suspension Systems 2579.1 Introduction 2579.2 Related Works 2599.3 Fault Diagnosis Problem Formulation for Semi-active ER Suspension Systems 2619.4 Fault Estimation Using LPV PI Observers 2659.5 FTC LPV Control of Semi-active Suspension Systems 2769.6 Conclusion 284Bibliography 28410 Lateral LPV Adaptive-Like Control of Automated Vehicles Adapted to Driver Performance 29310.1 Introduction 29310.2 LPV Observer-Based Control Structure for ADAS Systems 29410.3 Driver Fault Estimation Using a Discrete-Time LPV PI Observer 29510.4 Robust H∞∕LPV ADAS Strategy 30110.5 Simulation Results 30810.6 Conclusion 313Bibliography 313Index 317