Improved Robust and Optimal Performance of DC Servo Motor Using Model Predictive Control With Implementation

Position control of direct current motors remains one of the most important control problems in various application domains like robotics, automation in industries, and aviation. Traditionally, Proportional–Integral–Derivative based controllers are most popular for such scenarios, however due to their inability to handle constraints and are not being optimal and robust by design, they are not preferred in precision position tracking applications like antenna positioning, pitch angle control for wind turbine blades, solar tracking in photovoltaic panels etc. This calls for the need to employ some robust and high-precision controllers like model predictive control. The main objective of the work carried out is to present a better alternative for the position control problem for a DC servo motor plant using model predictive control. The optimization problem is formulated to minimize the cost function that penalizes position errors and input changes, along with the necessary constraints on output and inputs. The implementation of the proposed scheme is carried out both in simulations and with experimentation. In simulation, the scheme is verified using MATLAB/Simulink, and in experimentation on the real plant of Quanser's DC servo motor setup through Simulink real-time interface blocks. The obtained simulation and experimental results efficiently validate the proposed theoretical findings by gracefully achieving the required position trajectory tracking. Achieved results are also compared with standard PID, which confirms the superiority of model predictive control over PID control, especially in handling constraints and yielding better tracking performance without any overshoots and with the overall lesser control energy requirement.