Adaptive neural dynamic surface control of output constrained non-linear systems with unknown control direction

• Published in: IET control theory & applications Vol. 11; no. 17; pp. 2994 - 3003
• Main Authors: Zhang, Sainan, Tang, Zhongliang, Ge, Shuzhi Sam, He, Wei
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 24.11.2017
• Subjects: adaptive control; adaptive neural dynamic surface control; closed loop systems; closed‐loop system; constraint range; control system synthesis; DSC technology; iBLF; initial output signals; Integral barrier Lyapunov functions; Lyapunov methods; Lyapunov synthesis; neurocontrollers; nonlinear control systems; Nussbaum‐type dynamic gain algorithm; output constrained nonlinear systems; output constraint; parametric uncertainties; pure tracking error‐based barrier Lyapunov function; Research Article; semiglobal uniformly ultimately bounded control; uncertain control direction; uncertain systems; unknown system dynamics; Nussbaum-type dynamic gain algorithm; iBLF; uncertain systems; semiglobal uniformly ultimately bounded control; parametric uncertainties; adaptive control; control system synthesis; constraint range; Lyapunov synthesis; Integral barrier Lyapunov functions; closed loop systems; adaptive neural dynamic surface control; initial output signals; neurocontrollers; uncertain control direction; output constrained nonlinear systems; nonlinear control systems; DSC technology; pure tracking error-based barrier Lyapunov function; output constraint; unknown system dynamics; closed-loop system; Lyapunov methods
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2017.0556

This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non-linear systems, subject to unknown system dynamics and uncertain control direction. A Nussbaum-type dynamic gain algorithm is used to handle the effect of unknown control direction. Integral barrier Lyapunov functions (iBLFs) are directly utilised to tackle the effect of output constraint. The prominent feature of iBLFs is that the feasible initial output signals are relaxed to the whole constraint range compared with pure tracking errors-based barrier Lyapunov function. Also, the DSC technology is developed to avoid the explosion of complexity in traditional control design, and adaptive neural networks are adopted to estimate the uncertainty that comprises the unknown system dynamics and parametric uncertainties. By utilising Lyapunov synthesis, it is proven that the proposed control is able to guarantee semi-global uniformly ultimately bounded of all signals in the closed-loop system. Simulation results are provided to verify the effectiveness of the proposed approach.