Nonlinear MPC

As in traditional linear MPC, nonlinear MPC calculates control actions at each control interval using a combination of model-based prediction and constrained optimization. The key differences are:

  • The prediction model can be nonlinear and include time-varying parameters.

  • The equality and inequality constraints can be nonlinear.

  • The scalar cost function to be minimized can be a nonquadratic (linear or nonlinear) function of the decision variables.

Using nonlinear MPC, you can:

  • Simulate closed-loop control of nonlinear plants under nonlinear costs and constraints.

  • Plan optimal trajectories by solving an open-loop constrained nonlinear optimization problem.

To implement nonlinear MPC, create an nlmpc object, and specify:

By default, nonlinear MPC controllers solve a nonlinear programming problem using the fmincon function with the SQP algorithm, which requires Optimization Toolbox™ software. If you do not have Optimization Toolbox software, you can specify your own custom nonlinear solver. For more information on configuring the fmincon solver and specifying a custom solver, see Configure Optimization Solver for Nonlinear MPC.

You can simulate nonlinear MPC controllers:


The MPC Designer app does not support the design of nonlinear MPC controllers.

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