Adaptive neural network–based fault estimation for nonlinear systems with actuator faults in simultaneous multiplicative and additive forms

2021 ◽  
pp. 014233122110585
Author(s):  
Yan Zhao ◽  
Minhang Song ◽  
Xiangguo Huang ◽  
Ming Chen
Keyword(s):  
Neural Network ◽  
Fault Tolerant ◽  
Fault Estimation ◽  
Actuator Faults ◽  
Closed Loop System ◽  
Adaptive Neural Network ◽  
Neural Controller ◽  
Adaptive Parameter ◽  
Practical Engineering ◽  
Adaptive Neural Controller

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller.

scholarly journals Adaptive Fault Estimation and Fault Tolerant Control for Polynomial Systems: Application to Electronic and Mechanical Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
A. Messaoudi ◽  
H. Gassara ◽  
A. El Hajjaji
Keyword(s):  
Active Fault ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Fault Tolerant Control ◽  
Estimation Algorithm ◽  
Polynomial Systems ◽  
Fault Estimation ◽  
Actuator Faults ◽  
Closed Loop System ◽  
Mass Spring

This paper presents a sum of squares (SOS) approach for active fault tolerant control (AFTC) of nonlinear polynomial systems. A polynomial adaptive fault estimation algorithm for polynomial systems is firstly proposed. Then, sufficient conditions for the existence of the fault estimator are given in terms of SOS which can be numerically (partially symbolically) solved via the recently developed SOSTOOLS. Based on the obtained online fault estimation information, a fault-tolerant control strategy is designed for both compensating the effect of actuator faults in real time and stabilizing the closed-loop system. Finally, tunnel diode circuit and mass-spring-damper systems are used to demonstrate the applicability of the proposed approach.

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

2021 ◽  
pp. 095965182110131
Author(s):  
Guoqing Zhang ◽  
Shen Gao ◽  
Jiqiang Li ◽  
Weidong Zhang
Keyword(s):  
Neural Control ◽  
Fault Tolerant ◽  
Lyapunov Stability Theory ◽  
Actuator Faults ◽  
Closed Loop System ◽  
External Disturbances ◽  
Unmanned Surface Vehicle ◽  
Adaptive Parameters ◽  
Control Gain ◽  
Event Triggered

This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

Robust fault-tolerant control design for hypersonic vehicle with input saturation and actuator faults

2021 ◽  
pp. 095441002110412
Author(s):  
Zhong-Zhe Yue ◽  
Jing-Guang Sun
Keyword(s):  
Feedback Linearization ◽  
Fault Tolerant ◽  
Input Saturation ◽  
Lyapunov Stability Theory ◽  
Hypersonic Vehicle ◽  
Actuator Faults ◽  
Closed Loop System ◽  
External Disturbances ◽  
Longitudinal Tracking ◽  
Tangent Function

This study investigates the flight longitudinal tracking control problem of hypersonic vehicle in presence of the input saturation, external disturbances, model parametric uncertainties, and actuator faults. First, the velocity and altitude subsystem are established with disturbances based on the feedback linearization model. Second, two robust anti-saturation fault-tolerant controllers are designed for the velocity subsystem and altitude subsystem by the utilization of the tangent function, Nussbaum function, and adaptive nonlinear filter. Finally, Lyapunov stability theory is used to prove that the states of the closed-loop system are bounded. And, the effectiveness and robustness of the control strategy are proved by numerical simulations.

Improved Integral LOS Guidance and Path-Following Control for an Unmanned Robot Sailboat via the Robust Neural Damping Technique

2019 ◽  
Vol 72 (06) ◽  
pp. 1378-1398 ◽  
Author(s):  
Guoqing Zhang ◽  
Jiqiang Li ◽  
Bo Li ◽  
Xianku Zhang
Keyword(s):  
External Disturbance ◽  
Path Following ◽  
Basic Function ◽  
Lyapunov Theory ◽  
Neural Controller ◽  
Adaptive Parameter ◽  
Guidance Law ◽  
Path Following Control ◽  
Adaptive Neural Controller ◽  
Robust Adaptive

This paper introduces a scheme for waypoint-based path-following control for an Unmanned Robot Sailboat (URS) in the presence of actuator gain uncertainty and unknown environment disturbances. The proposed scheme has two components: intelligent guidance and an adaptive neural controller. Considering upwind and downwind navigation, an improved version of the integral Line-Of-Sight (LOS) guidance principle is developed to generate the appropriate heading reference for a URS. Associated with the integral LOS guidance law, a robust adaptive algorithm is proposed for a URS using Radial Basic Function Neural Networks (RBF-NNs) and a robust neural damping technique. In order to achieve a robust neural damping technique, one single adaptive parameter must be updated online to stabilise the effect of the gain uncertainty and the external disturbance. To ensure Semi-Global Uniform Ultimate Bounded (SGUUB) stability, the Lyapunov theory has been employed. Two simulated experiments have been conducted to illustrate that the control effects can achieve a satisfactory performance.

scholarly journals Active Fault-Tolerant Control of a Quadcopter against Time-Varying Actuator Faults and Saturations Using Sliding Mode Backstepping Approach

2019 ◽  
Vol 9 (19) ◽  
pp. 4010 ◽  
Author(s):  
Ngoc Phi Nguyen ◽  
Sung Kyung Hong
Keyword(s):  
Fault Diagnosis ◽  
Active Fault ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Lyapunov Theory ◽  
Fault Estimation ◽  
Time Varying ◽  
Actuator Faults ◽  
Active Fault Tolerant Control

Fault-tolerant control is becoming an interesting topic because of its reliability and safety. This paper reports an active fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) to handle actuator faults, disturbances, and input constraints. A robust fault diagnosis based on the H ∞ scheme was designed to estimate the magnitude of a time-varying fault in the presence of disturbances with unknown upper bounds. Once the fault estimation was complete, a fault-tolerant control scheme was proposed for the attitude system, using adaptive sliding mode backstepping control to accommodate the actuator faults, despite actuator saturation limitation and disturbances. The Lyapunov theory was applied to prove the robustness and stability of the closed-loop system under faulty operation. Simulation results show the effectiveness of the fault diagnosis scheme and proposed controller for handling actuator faults.

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