Adaptive Neural Control of Uncertain MIMO Nonlinear Pure-Feedback Systems via Quantized State Feedback

IEEE Access

Sung Jin Yoo¹  Byung Mo Kim¹ 

[1] School of Electrical and Electronics Engineering, Chung-Ang University, Dongjak-Gu, Seoul, South Korea;

关键词: Quantized state feedback control;    neural networks;    state quantization;    MIMO nonlinear systems;    pure-feedback form;   

DOI  :  10.1109/ACCESS.2022.3165567

来源: DOAJ

【 摘 要 】

We present an adaptive quantized state feedback tracking methodology for a class of uncertain multiple-input multiple-output (MIMO) nonlinear block-triangular pure-feedback systems with state quantizers. Uniform quantizers are considered to quantize all measurable state variables for feedback. Compared with the existing tracking approaches of MIMO lower-triangular nonlinear systems, the main contributions of the proposed strategy are developing (1) a quantized-state-feedback-based adaptive tracker in the presence of nonaffine interaction of states and control variables of MIMO systems and (2) an analysis strategy for quantized feedback stability using adaptive compensation terms to derive bounded quantization errors. In addition, the stability of the closed-loop system with quantized state feedback is analyzed based on the Lyapunov stability theorem. Finally, simulation examples, including interconnected inverted pendulums, are presented to validate the effectiveness of the proposed control strategy.

【 授权许可】

Unknown