• Design and simulate model predictive controllers using Model Predictive Control Toolbox.
    Use Model Predictive Control Toolbox™ to design and simulate model predictive controllers.
    Improve process performance by designing and implementing a model predictive controller. Use OPC Toolbox™ and System Identification Toolbox™ to collect the input-output data and create a plant model.
    Using model predictive control, the intelligent optimizer reduces emissions and cuts costs all while improving combustion efficiency. This session demonstrates the end-to-end optimization solution,...
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.
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.
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.
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.
  • Professor Alberto Bemporad, co-author of Model Predictive Control Toolbox, shares his insights into designing model predictive controllers.
    Professor Alberto Bemporad, co-author of Model Predictive Control Toolbox, talks about best practices for implementing model predictive controllers.