DSP Based Model Predictive Control for DC Motor

Abstract

This thesis presents a DSP based Model Predictive Control approach for controlling a DC motor. The MPC algorithm is designed to optimize the motor's performance by predicting its behavior over a finite time horizon and adjusting the control inputs accordingly. The proposed method provides improved performance in terms of faster response time, settling time, efficient tracking of reference trajectories and minimum steady-state errors. The system performance is evaluated under different operating conditions, including changes in sampling time, load torque, motor speed, and ability to handle constraints. The results show that the DSP based MPC approach provides better performance compared to traditional PID control methods. Further, the proposed method is implemented on a digital signal processor based hardware platform, and the results show that it is feasible for real time control applications. The suggested approach illustrates how MPC can be a viable solution for the precise and efficient regulation of DC motor in real-world scenarios