Observer-based neural control for MIMO pure-feedback non-linear systems with input saturation and disturbances - Liu, Wenhui; Lu, Junwei; Zhang, Zhengqiang; Xu, Shengyuan (IET control theory & applications, 2016)
This study considers the adaptive neural backstepping control for multiple-input and multiple-output pure-feedback systems subject to input saturation and disturbances. Neural networks are used to approximate the uncertain non-linear functions without any prior limited conditions. A non-linear disturbance observer and a state observer are constructed to design the output-feedback neural controller. A new coordinate transform is defined to handle the pure-feedback systems in the backstepping procedure. The proposed controller can make sure that all the state trajectories are ultimately bounded in the pure-feedback non-linear systems. An illustrative example is given to show the usefulness of the authors' designed new control method.