In this series, you'll learn how model predictive control (MPC) works, and you’ll discover the benefits of this multivariable control technique.
MPC uses a model of the system to make predictions about the system’s future behavior. MPC solves an online optimization algorithm to find the optimal control action that drives the predicted output to the reference. MPC can handle multi-input multi-output systems that may have interactions between their inputs and outputs. It can also handle input and output constraints. MPC has preview capability; it can incorporate future reference information into the control problem to improve controller performance.
This series also discusses MPC design parameters such as the controller sample time, prediction and control horizons, constraints, and weights. It also gives you recommendations for choosing these parameters. You'll learn about adaptive, gain-scheduled, and nonlinear MPCs, and you’ll get implementation tips to reduce the computational complexity of MPC and run it faster.
Finally, the series demonstrates examples for designing MPC controllers in MATLAB® and Simulink®.
Part 1: Why Use MPC? Learn about model predictive control (MPC). MPC handles MIMO systems with input-output interactions, deals with constraints, has preview capabilities, and is used in industries such as auto and aero.
Part 2: What Is MPC? Learn how model predictive control (MPC) works. MPC uses a model of the plant to make predictions about future plant outputs. It solves an optimization problem at each time step to find the optimal control action that drives the predicted plant output to the desired reference as close as possible.
Part 3: MPC Design Parameters To successfully control a system using MPC, you need to carefully select design parameters. Learn how to select the controller sample time, prediction and control horizons, and constraints and weights.
Part 4: Adaptive, Gain-Scheduled, and Nonlinear MPC Learn about the type of MPC controller you can use based on your plant model, constraints, and cost function. Options include the linear time-invariant, adaptive, gain-scheduled, and nonlinear MPC.
Part 5: How To Run MPC Faster Learn which techniques you can use to run MPC faster. The video outlines methods, such as explicit MPC and suboptimal solution, that you can implement for your applications with small sample times.
Part 6: How to Design an MPC Controller with Simulink and Model Predictive Control Toolbox Learn how to design an MPC controller for an autonomous vehicle steering system using Model Predictive Control Toolbox.
Part 7: Adaptive MPC Design with Simulink and Model Predictive Control Toolbox Learn how to deal with changing plant dynamics using adaptive MPC. This video uses an autonomous steering vehicle system example to demonstrate the controller’s design.
Part 8: Nonlinear MPC Design with Model Predictive Control Toolbox and FORCESPRO Learn how to design a nonlinear MPC controller for an automated driving application with Model Predictive Control Toolbox and Embotech FORCESPRO solvers.