This white paper introduces a systematic workflow for model-based automatic code generation for Model Predictive Control. Model Predictive Control is a closed loop implementation of optimal control that has become the alternative advanced control method to proportional-integral-derivative controllers in many industries.
This technical white paper presents an automatic workflow for implementing MPC controllers. The process uses MapleSim to generate a model of the system, whose dynamic equations are then extracted using Maple. The advantage of using a symbolic tool such as Maple is that the required procedures and their corresponding derivatives are automatically computed and optimized whenever there is a change in any of the dynamic equations representing the system. Automating the workflow using Maple’s symbolic computation engine saves time, removes human error, and produces highly optimized controller code.
