Industrial robot control covers nonlinearity, uncertainty and external perturbation considered in control laws design. Proportional and Derivative (PD) with gravity compensation control is well-known control used in manipulators to ensure global asymptotic stability for fixed symmetrical positive definite gain matrices. To enhance PD with gravity compensation controller performance, in this paper, we propose hybrid fuzzy PD control precompensation with gravity compensation, consisting of a fuzzy logic-based precompensator followed by hybrid fuzzy PD with gravity compensation controller. Hybrid fuzzy control is done by a Supervisory Hierarchical Fuzzy Controller (SHFC) for tuning conventional controller Proportional and Derivative gains based on actual tracking location and velocity error. Hierarchical hybrid fuzzy control consists of an intelligent upper supervisory fuzzy controller and a lower direct conventional PD controller. Numerical simulations using the dynamic model of a three DOF planar rigid robot manipulator with uncertainty show the effectiveness of the approach in trajectory tracking problems. Our results show that the proposal controller has performance superior to a conventional controller.
Imperialist Competitive Algorithm Optimised Adaptive Neuro Fuzzy Controller for Hybrid Force Position Control of an Industrial Robot Manipulator: A Comparative Study
