Adaptive Intelligent Tracking Control of Flexible-Joint Manipulator with Full-State Constraints

This paper investigates the intelligent neural adaptive trajectory tracking control problem for a flexible-joint manipulator with full-state constraints. The approach uses neural networks to approximate the system's uncertain nonlinear terms and integrates backstepping recursion with a tangent-type bar-rier Lyapunov function to develop an intelligent adaptive control method with comprehensive state constraints. The stability of the closed-loop system is demonstrated using Lyapunov stability theory, ensuring that all state variables remain within predetermined boundaries. The proposed method's feasibility and effectiveness are ultimately validated through simulation of the flexible-joint manipulator system.