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"Fault-tolerant control"
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Fault-tolerant cooperative control of unmanned aerial vehicles
\"This book focuses on the fault-tolerant cooperative control (FTCC) of multiple unmanned aerial vehicles (multi-UAVs). It provides systematic and comprehensive descriptions of FTCC issues in multi-UAVs concerning faults, external disturbances, strongly unknown nonlinearities, and input saturation. Further, it addresses FTCC design from longitudinal motions to attitude motions, and outer-loop position motions of multi-UAVs. The books detailed control schemes can be used to enhance the flight safety of multi-UAVs. As such, the book offers readers an in-depth understanding of UAV safety in cooperative/formation flight and corresponding design methods. The FTCC methods presented here can also provide guidelines for engineers to improve the safety of aerospace engineering systems. The book offers a valuable asset for scientists and researchers, aerospace engineers, control engineers, lecturers and teachers, and graduates and undergraduates in the system and control community, especially those working in the field of UAV cooperation and multi-agent systems.\"-- Back cover.
Book
Progressive Optimal Fault-Tolerant Control Combining Active and Passive Control Manners
This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified, and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. Simulation results verified the feasibility of the proposed algorithm and the validity of the proposed theory.
Journal Article
Robust static output-feedback controller design against sensor failure for vehicle dynamics
This study deals with the design of a robust fault estimation and fault-tolerant control for vehicle lateral dynamics subject to external disturbance and unknown sensor faults. Firstly, a descriptor state and fault observer is designed to achieve the system state and sensor fault estimates simultaneously. Secondly, based on the information of on-line fault estimates, a robust fault-tolerant controller based on static output-feedback controller (SOFC) design approach is developed. To provide linear matrix inequalities of less conservatism, the results are conducted in the non-quadratic framework dealing with unmeasurable premise variables case. Simulation results show the effectiveness of the proposed control approach when the vehicle road adhesion conditions change and the sideslip angle is unavailable for measurement.
Journal Article
Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization
In this paper, adaptive fault-tolerant control for multi-joint robot manipulators is proposed through the combination of synchronous techniques and neural networks. By using a synchronization technique, the position error at each joint simultaneously approaches zero during convergence due to the constraints imposed by the synchronization controller. This aspect is particularly important in fault-tolerant control, as it enables the robot to rapidly and effectively reduce the impact of faults, ensuring the performance of the robot when faults occur. Additionally, the neural network technique is used to compensate for uncertainty, disturbances, and faults in the system via online updating. Firstly, novel robust synchronous control for a robot manipulator based on terminal sliding mode control is presented. Subsequently, a combination of the novel synchronous control and neural network is proposed to enhance the fault tolerance of the robot manipulator. Finally, simulation results for a 3-DOF robot manipulator are presented to demonstrate the effectiveness of the proposed controller in comparison to traditional control techniques.
Journal Article
Study of Takagi-Sugeno fuzzy-based terminal-sliding mode fault-tolerant control
This study studies the fault-tolerant control (FTC) design based on the Takagi-Sugeno (T-S) fuzzy system models and terminal-sliding-mode control (TSMC). This hybrid scheme can keep the advantages of both methods. By using the T-S fuzzy models to approximate the original non-linear system, the online computation burden can be alleviated since most of the T-S parameters can be offline computed. Moreover, TSMC not only owns the merits, including robustness to uncertainties and/or disturbances, fast response and easy implementation, but also performs better than conventional sliding-mode control (SMC) since the system states of TSMC will converge in finite time to the control objective point, that is, equivalent point, after the system states intersect sliding surface. Both of the active and passive FTC design schemes are presented. The proposed analytical results are also applied to the FTC for the attitude stabilisation of a spacecraft. Simulation results demonstrate the benefits of the proposed scheme.
Journal Article
Cooperative Adaptive Formation Fault‐Tolerant Neural Control for Multiple Quadrotors With Full‐State Constraints
This paper investigates the cooperative time‐varying formation fault‐tolerant control problem for multiple quadrotors with unknown actuator faults and full state constraints. In order to ensure the safety and operability of quadrotors in the confined flight environment, a novel transformed function is first introduced to convert the original quadrotor systems into unconstrained equivalent systems, which increases the flexibility of the controller design. Then, a distributed kinematic control protocol and fault‐tolerant dynamic control protocol using the adaptive neural networks estimation technique are developed to guarantee the cooperative time‐varying formation of multiple quadrotors subject to uncertain parameters. Meanwhile, the unknown actuator loss of effectiveness and bias faults are compensated and the state variables of position subsystem and attitude subsystem can be maintained within the designed performance constraint sets even when actuator faults occur. Via Lyapunov stability theory, the cooperative formation fault‐tolerant performance analysis is presented. The proposed control strategy is validated through simulations. This paper investigates the cooperative time‐varying formation fault‐tolerant control problem for multiple quadrotors with unknown actuator faults and full state constraints. A distributed kinematic control protocol and fault‐tolerant dynamic control protocol using the adaptive neural networks estimation technique are developed to guarantee the cooperative time‐varying formation of multiple quadrotors subject to uncertain parameters. The validity of the proposed control strategy is verified by a simulation study.
Journal Article
Experimental Study on Support Vector Machine-Based Early Detection for Sensor Faults and Operator-Based Robust Fault Tolerant Control
Considering sensor faults for a thermoelectric cooler actuated by Peltier devices, this work proposes an operator-based robust nonlinear fault tolerant controller (FTC) integrated with early fault detection using a support vector machine (SVM). Firstly, a physical model is formulated based on the law of heat transfer, and the estimated model is derived based on Volterra identification. Then, an operator-based robust nonlinear control system is employed to compensate for uncertainties and to eliminate the effects of coupling. Furthermore, FTC integrated with SVM-based early fault detection is designed to improve the safety performance in the case of sensor faults. The simulation results indicate that SVM-based fault detection can shorten the detection time in comparison to the conventional method without the SVM classier. The experiment results are utilized to verify the tracking performance of the proposed FTC method in the case study.
Journal Article
IoT-enabled dependable control for solar energy harvesting in smart buildings
Efficiency and reliability have been essential requirements for energy generation in smart cities. This study presents the design and development of dependable control schemes for microgrid management, which can be seamlessly integrated into the management system of smart buildings. Here, to recover from failures in the solar energy system of a building microgrid, dependable controllers are proposed along with their hardware implementation. The system features the use of Internet of Things (IoT) as its core to coordinate the operation of multiple subsystems in a scalable manner. The control scheme uses a number of controllers cooperatively functioning via a token-based mechanism within the network to provide redundancy and thus reliability in solar tracking. The system exploits data from not only local in-situ sensors but also online sources via IoT networks for fault-tolerant control. Experiments conducted in a 12-storey building indicate that the harvested solar energy meets the design requirement while the control reliability is maintained in face of communication or hardware disruptions. The results confirmed the validity of the proposed approach and its applicability to energy management in smart buildings.
Journal Article
Koopman fault‐tolerant model predictive control
This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour.
Journal Article
Fixed‐Time Fault‐Tolerant Dynamic Formation Control for Heterogeneous Multi‐Agent Systems With Communication Link Faults for Collaborative Wildfire Monitoring
This paper addresses the problem of heterogeneous multi‐agent systems (HMAS), comprising multiple uncrewed ground vehicles (UGVs) and multiple uncrewed aerial vehicles (UAVs), collaboratively monitoring the wildfire in the presence of actuator faults and communication link faults during the fire monitoring mission. It presents a fixed‐time fault‐tolerant dynamic formation control scheme designed for HMAS, with the objective of monitoring either the circular or elliptical propagation of a wildfire. The paper adopts a fixed‐time extended state observer (FxESO) to estimate the multi‐source disturbances arising from external disturbances and actuator faults, ensuring fixed‐time convergence of the estimation errors of the FxESO. By utilizing the Lyapunov candidate theorem, the collaborative tracking errors will converge to zero in fixed time, regardless of the initial position, ensuring that all agents in HMAS monitor the dynamic wildfire perimeter. Comparative simulation results are presented to illustrate the effectiveness of the proposed control scheme. The paper proposes a fixed‐time fault‐tolerant dynamic formation control scheme for heterogeneous multi‐agent systems (HMAS) consisting of both uncrewed ground vehicles (UGVs) and uncrewed aerial vehicles (UAVs) to collaboratively monitor wildfires in the presence of actuator faults and communication link faults. It utilizes a fixed‐time extended state observer (FxESO) to estimate disturbances and actuator faults, ensuring fixed‐time convergence of the estimation errors. The paper demonstrates the effectiveness of the proposed control scheme through comparative simulation results, showing that the collaborative tracking errors converge to zero in fixed time, enabling all agents in HMAS to monitor the dynamic wildfire perimeter regardless of their initial positions.
Journal Article