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Understanding Model Predictive Control, Part 5: How To Run MPC Faster

This video starts by providing quick tips for implementing MPC for fast applications. - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M To reduce the complexity of MPC calculations, you can try to use model order reduction techniques, use shorter prediction and control horizons, reduce the number of constraints, and use lower-precision data representations and operations. - Explicit MPC Design: http://bit.ly/2GI3FBp - Use Suboptimal Solution in Fast MPC Applications: http://bit.ly/2L05efn If you need to further decrease the sample time for your fast applications, you can use explicit MPC or suboptimal solution. Explicit MPC requires fewer run-time computations than traditional MPC by using optimal solutions precomputed offline. You can guarantee the worst-case execution time for your MPC controller by applying a suboptimal solution after the number of iterations exceeds a specified maximum value. -How to Design an MPC Controller with Simulink and Model Predictive Control Toolbox: http://bit.ly/2Gvv0qe - Adaptive MPC Design with Simulink and Model Predictive Control Toolbox: http://bit.ly/2GsL5Nu


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