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

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
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