Fuzzy gain scheduling for position control of a robot manipulator

Abstract

This paper presents an interactive procedure for controller design for nonlinear system by integrating available classical as well as modern tools such as fuzzy logic, and neural networks. The proposed approach is based on quasi-linear dynamic models of the plant. Classical optimal controllers for each set of operating conditions were developed. These controllers are used to construct a single fuzzy-logic gain scheduling-like controller. Adaptive-neuro-fuzzy inference system was used to construct the rules for the fuzzy gain schedule. This will guarantee the continuos change in the gains as the system parameters change in time or space. This procedure is systematic and can be used to design controllers for many nonlinear systems. Two degrees of freedom (dof) planar manipulator was chosen to show the effectiveness of the proposed approach. A robot manipulator is inherently unstable and displays a strong nonlinearity. The resulting system is stable for different reference trajectories. The system is also robust for wide range of driving frequencies of the input. This system is able to deal with slow as well as fast varying systems, which is a significant improvement on conventional gain scheduling.