Adaptive fuzzy voltage-based backstepping tracking control for uncertain robotic manipulators subject to partial state constraints and input delay

The aim of this paper is to tackle the problem of adaptive fuzzy voltage-based tracking control for uncertain electrically driven robotic manipulators subject to input delay and partial state constraints in a unified framework. With the aid of barrier Lyapunov function-based backstepping method and adaptive fuzzy approximators, the proposed method is constructed for uncertain robotic systems in the framework of voltage control strategy. This is intended to convert robot control problem to motor control problem. Based on input integral technique, a new variable is introduced for the system such that the input-delayed robotic system is turned to the non-delayed robotic system. Furthermore, the number of adaptive learning parameters is free from the number of subsystems. In other words, only one adaptive parameter is adjusted online for each joint to reduce computational burden; hence, a new adaptive fuzzy voltage tracking control law is developed to ensure that all variables of the closed-loop system are semi-globally uniformly ultimately bounded. The tracking error of joint positions also converges to a small neighborhood around the origin such that the constraints on the joint angular positions and velocities are not transgressed during operation. Various scenarios for numerical simulations are given to show the potential of the proposed control algorithm when applied to a robot manipulator driven by permanent magnet dc motors.