Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics

This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.

I. Introduction

Over the last decade, there has been tremendous progress in the development of controllers for robot manipulators, which have inherent physical constraints such as saturation nonlinearities of actuators and friction phenomena at the robotic joints. Saturation may lead to electromechanical actuator damage, and friction will cause steady-state tracking error and oscillations. These constraints deteriorate the system performance and stability, and it is difficult to establish a precise mathematical model for the design of a model-based control system. Santibanez et al. [1] proposed a novel global asymptotic stable set-point fuzzy controller with bounded torques for robot manipulators. Although the friction effect and the phenomenon of torque saturation were considered in this robot dynamic model, some constrained conditions and prior system information were required in the control process. Kim [2] developed the output feedback tracking control of robot manipulators with model uncertainty by using adaptive fuzzy logic. However, partial system parameters were used in the designed control law, and the total errors, including the observation error, tracking error, and fuzzy estimation error, were only ensured to be uniformly ultimately bounded. Gao and Selmic [3] investigated a neural network control of a class of nonlinear systems with actuator saturation. Unfortunately, in order to focus on the effect of actuator saturation nonlinearity, gravity and joint friction were neglected in its system model. Jafarov et al. [4] introduced a new variable structure proportional–integral–differential (PID)-controller design for robot manipulators. Even though the global asymptotic stability of the controlled robot system was analyzed, the bounds of system parameter matrices need to be known in the control design.