Model Predictive Control Design Parameters | Understanding MPC, Part 3
This video provides recommendations for choosing the controller sample time, prediction and control horizons, and constraints and weights. To successfully control a system using an MPC controller, you need to carefully select its design parameters. Watch all of the videos in this series about Understanding Model Predictive Control: • Understanding Model Predictive Control Download this hands-on MPC virtual lab to practice design of model predictive controllers for an autonomous vehicle steering system: https://bit.ly/MPC-virtual-lab Learn how model predictive control (MPC) works: Model Predictive Control Toolbox: http://bit.ly/2xgwWvN What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M Design Controller Using MPC Designer: http://bit.ly/2GI2lhV Related Resources: How to Design Model Predictive Controllers: http://bit.ly/2M0BOd9 Choose Sample Time and Horizons: https://bit.ly/3QPS5GL Specify Constraints: https://bit.ly/3E5AXoy Tune Weights: http://bit.ly/2LXl72r 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 Watch more MATLAB Tech Talks: http://bit.ly/2rTc8Yp Get a free MATLAB Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's New in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
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