The Tracking Control of Mechanical Systems by an Adaptive Pole Placement Controller Combining with a Neural Network

Abstract In this paper, an adaptive controller of a plant with unknown parameters and unknown nonlinear frictional force is proposed and applied to the control of a single link robot. The measurement for control is only the motor angle. The proposed adaptive controller is composed of an adaptive pole placement controller and a neural network. The neural network is designed to compensate the nonlinear frictional force and linearize the robot plant. To improve the efficiency of learning of the neural network, an inverse model is introduced. Several considerations are made to improve the robustness of the controller. Many experiments are carried out. The experimental results show the effectiveness of developed control system.