Optimal adaptive neural PI full-order sliding mode control for robust fault tolerant control of uncertain nonlinear system

• Published in: European journal of control Vol. 54; pp. 22 - 32
• Main Authors: Van, Mien, Do, Xuan Phu
• Format: Journal Article
• Language: English
• Published: Philadelphia Elsevier Ltd 01.07.2020; Elsevier Limited
• Subjects: Adaptive control; Algorithms; Bat algorithm; Control theory; Controllers; Design; Fault diagnosis; Fault tolerance; Fault tolerant control; Full-order sliding mode; Fuzzy logic; Neural network; Neural networks; Nonlinear control; Nonlinear systems; Optimization; PI controller; Robust control; Sliding mode control; Spacecraft attitude control; System dynamics; Fault diagnosis; Bat algorithm; Fault tolerant control; Neural network; PI controller; Nonlinear systems; Full-order sliding mode
• ISSN: 0947-3580; 1435-5671
• DOI: 10.1016/j.ejcon.2019.12.005

•A PFTC based on a novel PI-FOSM controller, which combines a PI-FOSM sliding surface and a continuous control law, is proposed. The proposed FTC is designed via the passive manner so that an additional FD observer is not required.•The crucial parameters of the proposed PI-FOSM controller, such as proportional and integral gains, are optimally selected offline using BA so that the nearly optimal performance of the system can be achieved.•The unknown system dynamics is approximated using an adaptive RBFNN so that the proposed controller does not require an exact model of the dynamics system.•Compared to other existing robust FTC techniques, the proposed approach provides several merits such as strong robustness against disturbance, system uncertainties and faults, fast convergence, low steady state error and chattering-free. This paper proposes a robust fault tolerant control scheme for a class of second-order uncertain nonlinear systems. First, a novel PI full-order sliding mode (PI-FOSM) control, which integrates a new PI-FOSM sliding surface and a continuous control law, is developed. The crucial parameters of the controller are optimally selected by Bat algorithm so that the nearly optimal performance of the controller can be achieved. In addition, the unknown system dynamics is approximated by using a radial basic function neural network (RBFNN) so that the proposed controller does not require an exact model of the system. Compared with other existing sliding mode controllers for fault tolerant control system, the proposed method provides very strong robustness, low oscillation, fast convergence and high precision. The superior performance of the proposed robust fault tolerant controller is proved through simulation results for attitude control of a spacecraft.