Design of a Model Predictive Control System for Constrained DC Motor Applications

In this paper, we explore the design methodology for a Model Predictive Control (MPC) system applied to constrained DC motor applications. The design process begins with analyzing system requirements and performance objectives, including torque limitations, speed ranges, and power constraints. We then demonstrate the selection of appropriate MPC algorithms (such as linear or quadratic MPC variants) and parameter tuning techniques using MATLAB's Model Predictive Control Toolbox, with emphasis on constraint handling through cost function formulation and horizon settings.

The implementation phase covers detailed simulation and testing procedures using Simulink models integrated with MPC controllers. This includes code examples for designing prediction models based on DC motor transfer functions, implementing constraint boundaries in MPC object configuration, and validating system robustness through disturbance rejection tests. Key functions like mpc() for controller creation and sim() for closed-loop simulation are explained with practical parameter settings.

Finally, we discuss real-world engineering applications and potential enhancements such as adaptive MPC for varying load conditions, embedded code generation using MATLAB Coder, and optimization techniques for improving computational efficiency in resource-limited hardware deployments.