Application of Model Predictive Controller for 2-DOF robot manipulator

Many of the industrial processes are difficult to model because of their complex behavior, influent characteristics and operational conditions. Control of robot manipulator for industrial applications is considered as one of the challenging tasks. In this paper, Model Predictive Controller (MPC), a class of advanced control technique is proposed in order to control the motion of the revolute joints. MPC is an optimal control strategy based on numerical optimization. Future control inputs and future plant responses are predicted using a system model and optimized at regular intervals with respect to a performance index. Prediction is to determine the future value of the output variables based on available information. This prediction can be used in the design of control laws for better performance of the control systems. The suggested predictive control approach uses an objective function centered on output estimates over a prediction horizon, and hence error is decreased by a selection of operated variable over a control horizon. The performance of the angular motion of the 2-DOF robot link is analyzed with various set point changes in terms of torque. The proposed controller has been verified and validated using satisfactory simulation results of a model of an industrial robot manipulator.