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

Abstract

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. INDEX TERMSQuantized state feedback control, neural networks, state quantization, MIMO nonlinear systems, pure-feedback form. BYUNG MO KIM received the B.S. and M.S. degrees from the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South Korea, in 2019 and 2021, respectively. His current research interests include non-linear adaptive control and intelligent control using neural networks. SUNG JIN YOO (Member, IEEE) received the B.S., M.S., and Ph.D. degrees in electrical and electronic engineering from