In this paper, we propose a novel intelligent control scheme for a class of discrete-time nonlinear uncertain systems operating under multiple environments/control situations. First, based on the deterministic learning theory, artificial neural networks (NNs) are employed to accurately learn/identify the uncertain system dynamics under each individual environment. The learned knowledge is then utilized to: (i) achieve improved control performance by developing a family of experience-based controllers (EBCs), each of which is tailored to an individual environment; and (ii) determine real-time activation of the EBCs by developing a pattern recognition mechanism for online identifying the active control situation. In addition, a robust quasi-sliding mode controller is further designed and embedded in the overall control scheme to guarantee system stability during the transition process among multiple environments. The novelty of the proposed control scheme lies in its intelligent capabilities of knowledge acquisition and re-utilization in real-time control, enabling self-adaption to uncertain changing control environments. A simulation example is included to verify the effectiveness of the proposed results.

This content is only available via PDF.
You do not currently have access to this content.