SPONSORED White Paper: Nonlinear Model Predictive Control: Model-Based Automatic Code Generation

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.

Click here to download the white paper.

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