Adaptive Fault Compensation Research Articles

Improved Adaptive Fault-Tolerant Control for a Quadruple-Tank Process with Actuator Faults

In this paper, a modified output–feedback multivariable model reference adaptive control (MRAC) scheme is proposed for a valve-actuated quadruple-tank process with actuator faults. First, a controller parametric model with unknown plant parameters is established, and actuator parameters are determined using a unified actuator fault model. Second, some existing proper modifications that allow MRAC to gain the ability of actuator fault compensation are given. Then final adaptive fault compensation controller is constructed by utilizing existing results and a new adaptation law that is based on a method first used for optimization purposes. The resulting controller allows increasing the learning rate of the adaptation algorithm without causing high-frequency oscillation in control signal and eventually leads to a faster smooth postfault transient response and less reference model tracking error. The stability of the proposed controller is proved such that all closed-loop signals are bounded and tracking erro...

Decentralised fault compensation of time‐delayed interactions and dead‐zone actuators for a class of large‐scale non‐linear systems

An approximation-based decentralised adaptive fault compensation (FC) problem is addressed for large-scale non-linear time-delay systems with unknown faults in both time-delayed C 1 non-linear interconnections and non-symmetric dead-zone actuators. Error surfaces constrained by prescribed performance bounds, which characterise the convergence rate and maximum overshoot of control errors, are presented to provide predefined bounds of control errors. Then, we design a memoryless decentralised FC control system using the constrained-surfaces-based dynamic surface design methodology, without constructing a dead-zone inverse and requiring the information about dead-zone parameters and time-delayed interactions. For the compensator design, the function approximation technique using neural networks is applied to adaptively estimate unknown non-linear effects and changes in model dynamics because of time-delayed interaction and dead-zone actuator faults. It is shown from Lyapunov stability theorem that all the error surfaces are preserved within the prescribed performance bounds and finally converge to an adjustable neighbourhood of the origin. Therefore guaranteed transient performance is achieved at the moment the faults occur.

Adaptive backstepping control for a hypersonic vehicle with uncertain parameters and actuator faults

For a hypersonic vehicle with redundant elevators, an adaptive backstepping fault-tolerant control scheme is designed for its longitudinal motion model. In the backstepping design, the dynamic surface control strategy is adopted to eliminate the explosion of complexity in calculating the derivatives of virtual control inputs by introducing two first-order filters. An appropriate controller structure is proposed and the matching conditions are derived for adaptive fault compensation for the vehicle with redundant elevators subject to uncertain actuator faults. The adaptation laws are designed to update parameter estimates to implement the adaptive controller. The closed-loop stability under the proposed adaptive actuator fault compensation scheme is analyzed. Both analytical and simulation results are given to verify the effectiveness of the design and demonstrate the desired performance for altitude and velocity control.

Adaptive fault compensation control for a class of nonlinear systems with unknown time-varying delayed faults

An adaptive approximation design for the fault compensation (FC) control is addressed for a class of nonlinear systems with unknown multiple time-delayed nonlinear faults. The magnitude and occurrence time of the multiple faults with unknown time-varying delays are unknown. The function approximation technique using neural networks is employed to adaptively approximate the unknown nonlinear effects and changes in model dynamics due to the time-delayed faults. We design an adaptive memoryless FC control system with a prescribed performance bound to compensate the faults and to guarantee the transient performance of the tracking error from unexpected changes of system dynamics. The adaptive laws for neural networks and the bound of residual approximation errors are derived using the Lyapunov stability theorem, which are used for proving that the tracking error is preserved within the prescribed performance bound regardless of unknown multiple time-delayed nonlinear faults. Simulation examples are presented for illustrating the effectiveness of the proposed control methodology

A Mixed H/H∞ LPV Approach to Adaptive Fault Compensation for a Nonlinear UAV

The current study is motivated by the need to implement a linear model-based fault detection and isolation/ diagnosis (FDD) methodology and a corresponding active fault tolerant control (FTC) scheme onto a nonlinear aircraft system directly without control law reconfiguration The nonlinear model is expressed in linear parameter varying (LPV) form. The mixed quadratic H/H∞ LPV observer is then developed to maximize the robustness of the residual to uncertainty or disturbances as well as achieving the specific minimum sensitivity of the residual to faults, whilst the fault estimation can also be achieved for active on-line FTC via adaptive control. Instead of constant observer gains, parameter-varying gains are used in the LPV observer and the free design parameters are parameterized via a generalized inverse to improve the conservatism of the robust solution. The FDD approach together with FTC scheme developed is then applied in non-linear simulation to the example of faults in the yaw rate sensor of a UAV aircraft (Machan).

An iterative learning observer for fault detection and accommodation in nonlinear time-delay systems

This article addresses fault detection, estimation, and compensation problem in a class of disturbance driven time delay nonlinear systems. The proposed approach relies on an iterative learning observer (ILO) for fault detection, estimation, and compensation. When there are no faults in the system, the ILO supplies accurate disturbance estimation to the control system where the effect of disturbances on estimation error dynamics is attenuated. At the same time, the proposed ILO can detect sudden changes in the nonlinear system due to faults. As a result upon the detection of a fault, the same ILO is used to excite an adaptive control law in order to offset the effect of faults on the system. Further, the proposed ILO-based adaptive fault compensation strategy can handle multiple faults. The overall fault detection and compensation strategy proposed in the paper is finally demonstrated in simulation on an automotive engine example to illustrate the effectiveness of this approach. Copyright © 2005 John Wiley & Sons, Ltd.