Real-Time Implementation of Neuro Adaptive Observer-Based Robust Backstepping Controller for Twin Rotor Control System

摘要

In this paper, a robust backstepping controller based on the neuro adaptive observer for the twin rotor multiple-input-multiple-output (MIMO) system is designed and implemented in real time. The twin rotor MIMO system (TRMS) belongs to a class of nonlinear uncertain system having unstable, coupled dynamics. Nonlinearities of the TRMS are estimated using Chebyshev neural network. A tuning scheme based on Lyapunov theory of stability is developed which can guarantee the boundedness of tracking error and weights of the neural network. The proposed observer-based control guarantees the stability of the closed-loop adaptive system and the tracking errors converge to small residual sets in the presence of constraints on the control input. The effectiveness of the proposed observer-based robust controller is illustrated through simulation and experimental results. The real time implementation has been carried out on the real-time TRMS using MATLAB real-time tool box and Advantech PCI1711 card...