Data input-output adaptive controller based on IF-THEN rules for a class of non-affine discrete-time systems: The robotic plant

Abstract

A parallel structure of data input-output adaptive controllers is developed to control the 7-DOF robotic arm. This robotic system is considered as a class of unknown nonlinear discrete-time systems. The proposed controller is designed by an adaptive scheme called fuzzy rules emulated network(FREN). This network requires only the human knowledge regarding to the robotic arm in the format of IF-THEN rules. Those IF-THEN rule can be created by the general basic knowledge such as IF we need to move the robotic arm to the position which is far from the current position THEN we can apply high angular velocity. Furthermore, the mathematical model can be neglected because the robotic arm is considered as a grey box operated only the set of input-output signals. The closed-loop stability can be guaranteed by using general assumptions based on discrete-time Lyapunov direct method. The convergence analysis is introduced for both adjustable parameters inside FREN and the tracking error signal. The experimental system with Mitsubishi PA-10 robotic arm is developed to control all 7 joints with 7 FRENs connected as a parallel structure.