Fault Diagnosis and Fault-Tolerant Control of Robotic and Autonomous Systems (Control, Robotics and Sensors)

Description

Robotic systems have experienced exponential growth thanks to their incredible adaptability. Modern robots require an increasing level of autonomy and the capability to interact with humans. This book addresses the challenges of increasing and ensuring reliability and safety of modern robotic and autonomous systems. The book provides an overview of research in this field to-date, and addresses advanced topics including fault diagnosis and fault-tolerant control, and the challenging technologies and applications in industrial robotics, robotic manipulators, mobile robots, and autonomous and semi-autonomous vehicles. Chapters cover the following topics: fault diagnosis and fault-tolerant control of unmanned aerial vehicles; control techniques to deal with the damage of a quadrotor propeller; observer-based LPV control design of quad-TRUAV under rotor-tilt axle stuck fault; an unknown input observer based framework for fault and icing detection and accommodations in overactuated unmanned aerial vehicles; actuator fault tolerance for a WAM-V catamaran with azimuth thrusters; fault-tolerant control of a service robot; distributed fault detection and isolation strategy for a team of cooperative mobile manipulators; nonlinear optimal control for aerial robotic manipulators; fault diagnosis and fault-tolerant control techniques for aircraft Systems; fault-tolerant trajectory tracking control of in-wheel motor vehicles with energy efficient steering and torque distribution; nullspace-based input reconfiguration architecture for over-actuated aerial vehicles; data-driven approaches to fault-tolerant control of industrial robotic systems. Cover Contents List of contributors About the editors Foreword 1 Fault diagnosis and fault-tolerant control of unmanned aerial vehicles 1.1 Introduction 1.1.1 Unmanned aerial vehicle 1.1.2 Fault detection and diagnosis 1.1.3 Fault-tolerant control 1.2 Modeling of an unmanned quadrotor helicopter 1.2.1 Kinematic equations 1.2.2 Dynamic equations 1.2.3 Control mixing 1.2.4 Actuator fault formulation 1.3 Active fault-tolerant control 1.3.1 Problem statement 1.3.2 Adaptive sliding mode control 1.3.3 Construction of reconfigurable mechanism 1.4 Simulation results 1.4.1 Fault estimation and accommodation results 1.5 Conclusions References 2 Control techniques to deal with the damage of a quadrotor propeller 2.1 Introduction 2.2 Problem statement 2.3 Modeling 2.3.1 Quadrotor 2.3.2 Birotor 2.4 Control design 2.4.1 PID control scheme 2.4.2 Backstepping control scheme 2.5 Numerical simulations 2.5.1 Description 2.5.2 Case study 2.6 Conclusion Acknowledgments References 3 Observer-based LPV control design of quad-TRUAV under rotor-tilt axle stuck fault 3.1 Introduction 3.2 Quad-TRUAV and nonlinear modeling 3.3 LPV control analysis 3.3.1 Polytopic LPV representation 3.3.2 Closed-loop analysis with observer-based LPV control 3.4 Observer-based LPV control for the quad-TRUAV 3.4.1 Observer-based LPV controller synthesis 3.4.2 Inverse procedure design 3.5 Fault-tolerant design 3.5.1 Actuator stuck fault 3.5.2 Degraded model method for FTC 3.6 Numerical results 3.6.1 Fault-free results 3.6.2 FTC results under fault 3.7 Conclusions Acknowledgments References 4 An unknown input observer-based framework for fault and icing detection and accommodation in overactuated unmanned aerial vehicles 4.1 Introduction 4.2 Vehicle model 4.2.1 Linearization 4.2.2 Measured outputs 4.2.3 Control allocation setup 4.2.4 Wind disturbance 4.3 Icing and fault model 4.4 Unknown input observer framework 4.5 Diagnosis and accommodation 4.5.1 Detection and isolation in UAVs using UIOs 4.5.1.1 Full information case 4.5.1.2 Partial information case 4.5.2 Control allocation-based icing/fault accommodation 4.5.2.1 Effector icing: control reconfiguration 4.5.2.2 Airfoil leading edge icing: automated deicing system 4.6 Enhanced quasi-LPV framework 4.6.1 Nonlinear embedding 4.6.2 LPV unknown input observer 4.6.3 Application to the UAV fault/icing diagnosis 4.7 Illustrative example: the Aerosonde UAV References 5 Actuator fault tolerance for aWAM-V catamaran with azimuth thrusters 5.1 Introduction 5.2 Mathematical model 5.2.1 Dynamics 5.2.2 Actuator faults and failures 5.3 Control system architecture in the failure-free scenario 5.3.1 Control law 5.3.2 Control allocation 5.3.3 Control policy 5.4 Control reconfiguration in the presence of failures 5.4.1 Failure on S azimuth thruster 5.4.2 Blocked angle on S azimuth thruster 5.4.3 Other cases 5.5 Simulation results 5.5.1 Scenario I – fault-free actuators 5.5.2 Scenario II – double thruster faults 5.5.3 Scenario III – fault and failure on thrusters 5.5.4 Scenario IV – stuck and faulty thruster 5.5.5 Discussion of results 5.6 Conclusion References 6 Fault-tolerant control of a service robot 6.1 Introduction 6.1.1 State of the art 6.1.2 Objectives 6.2 Takagi–Sugeno model 6.2.1 Robot model 6.2.2 Takagi–Sugeno formulation 6.3 Control design 6.3.1 Parallel distributed control 6.3.2 Optimal control design 6.4 Fault and state estimation 6.4.1 Robust unknown input observer 6.4.2 Fault concept and design implications 6.4.3 Fault estimation and compensation 6.5 Fault-tolerant scheme 6.6 Application results 6.6.1 Basic control structure with the Luenberger observer 6.6.2 Basic control structure with RUIO 6.6.3 Complete fault-tolerant control scheme 6.7 Conclusions Acknowledgement References 7 Distributed fault detection and isolation strategy for a team of cooperative mobile manipulators 7.1 Introduction 7.2 Mathematical background and problem setting 7.2.1 Robot modeling 7.2.2 Communication 7.2.3 Problem description 7.3 Observer and controller scheme 7.3.1 Collective state estimation 7.3.2 Observer convergence 7.4 Fault diagnosis and isolation scheme 7.4.1 Residuals in the absence of faults 7.4.2 Residuals in the presence of faults 7.4.3 Detection and isolation strategy 7.5 Experiments 7.6 Conclusions Acknowledgment References 8 Nonlinear optimal control for aerial robotic manipulators 8.1 Introduction 8.2 Dynamic model of the aerial robotic manipulator 8.3 Approximate linearization of the model of the aerial robotic manipulator 8.4 Differential flatness properties of the aerial robotic manipulator 8.5 The nonlinear H-infinity control 8.5.1 Tracking error dynamics 8.5.2 Min–max control and disturbance rejection 8.6 Lyapunov stability analysis 8.7 Robust state estimation with the use of the H-Kalman filter 8.8 Simulation tests 8.9 Conclusions References 9 Fault diagnosis and fault-tolerant control techniques for aircraft systems 9.1 Introduction 9.2 Aircraft model simulator 9.3 Active fault-tolerant control system design 9.3.1 Fault diagnosis module 9.3.2 Fault accommodation strategy 9.4 Simulation results 9.4.1 Fault diagnosis filter design 9.4.2 NLGA-AF simulation results 9.4.3 AFTCS performance 9.5 Conclusion References 10 Fault-tolerant trajectory tracking control of in-wheel motor vehicles with energy-efficient steering and torque distribution 10.1 Trajectory-tracking controller design 10.1.1 Vehicle modeling 10.1.2 Reconfigurable LPV controller design 10.2 Fault-tolerant and energy optimal control synthesis 10.2.1 Control architecture 10.2.2 Fault-tolerant reconfiguration 10.2.3 Energy optimal reconfiguration 10.2.4 Efficient wheel torque distribution 10.3 Electric motor and battery model 10.3.1 Lithium-ion battery 10.3.2 Battery pack 10.3.3 Motor model 10.4 Simulation results 10.5 Conclusion References 11 Nullspace-based input reconfiguration architecture for over-actuated aerial vehicles 11.1 Inversion-based nullspace computation for parameter-varying systems 11.1.1 Nullspace of a linear map 11.1.2 Memoryless matrices 11.1.3 LPV systems 11.2 Geometry-based nullspace construction 11.2.1 Parameter-varying invariant subspaces 11.2.2 Nullspace construction for LPV systems 11.3 Control input reconfiguration architecture for compensating actuator failures 11.4 Reconfigurable fault-tolerant control of the B-1 aircraft 11.4.1 The non-linear flight simulator 11.4.2 Construction of the LPV model 11.4.3 Actuator inversion and nullspace computation 11.4.4 Fault signal tracking 11.4.5 Simulation results 11.4.6 Robustness analysis 11.5 Conclusion Acknowledgements References 12 Data-driven approaches to fault-tolerant control of industrial robotic systems 12.1 Background 12.2 Introduction and motivation 12.3 Data-driven control framework based onYoula parameterization 12.3.1 System description and preliminaries 12.3.2 Youla parameterization of all stabilizing controllers 12.3.3 Plug-and-play control framework 12.4 Reinforcement learning-aided approach to fault-tolerant controller design 12.4.1 Applying RL to control system design 12.4.2 Reward function design 12.4.3 RL-based solution to Youla parameterization matrix 12.5 Simulation study 12.5.1 Simulation setup 12.5.2 Results and discussion 12.6 Open questions about the framework and future work Appendix A References 13 Conclusions Index Back Cover