In this paper, we deal with the design of a model predictive control (MPC) for precise speed servo control of DC motor systems. The proposed controller is designed in the form of optimal control that calculates and outputs the optimized control input under constraints for each sampling. In particular, MPC designs the control inputs in advance for each sampling and predicts the outputs using them. Thus, it shows excellent control performance even in the case of disturbance or model uncertainty. The effectiveness of the proposed controller was demonstrated through computer simulations using MATLAB/Simulink and DC motor experimental system using real time controller. Moreover, the effectiveness of the proposed controller was confirmed by comparing its control performance with PID controller, which was tested under the same experimental condition as the MPC.

The predictive control system using model-based predictive control is a very effective way to optimize the present inputs considering the states and future errors of the reference trajectory, but it has a drawback in that a control input matrix must be repeatedly calculated with a long calculation time at every sampling for minimizing future errors in a predictive interval. In this study, we applied the neural network simulating the predictive control method for the trajectory tracking control of the mobile robot to reduce complex control method and computation time which are the disadvantage of predictive control. In addition, the neural network showed excellent performance by the generalization even for a different reference trajectory. Therefore, The controller is designed by modeling the model-based predictive control gains for the reference trajectory using a neural networks. Through the computer simulation, the proposed control method showed better performance than the general predictive control method.