Adaptive Stabilization Control for Second-order Strict Feedback System with Quantized Input via Fully Actuated System Method

This paper focuses on investigating the issue of adaptive fuzzy stabilization control for a specific class of uncertain second-order strict feedback system with quantized input via fully actuated system method. In order to deal with second-order systems directly and efficiently, we take the fully actuated system method. It is unnecessary to convert the second-order system into the first-order system, thereby significantly reducing the computational load. To effectively overcome the challenges posed by quantized input, the decomposition of hysteresis quantizer is used. It can effectively suppress high-frequency noise and deal with power reduction, and has good dynamic response characteristics. Moreover, it can greatly reduce the cost and is suitable for many low-cost, high-performance real-time control systems. To deal with the uncertainty in the system, fuzzy logic system technology is adopted, and the calculation amount is reduced by tunning function to avoid the occurrence of over-parametrization phenomenon and make the controller have good stability. The proposed quantization controller ensures that all signals in the closed-loop system remain bounded, thereby achieving stabilization control. Finally, the effectiveness and feasibility of the proposed method are further demonstrated through simulations on the single-link robot arm model. This paper offers a viable solution to the control issues in systems with uncertainty and quantized input.