Gain-scheduled model predictive control switches between a predefined set of MPC controllers, in a coordinated fashion, to control a nonlinear plant over a wide range of operating conditions. Use this approach if a single prediction model cannot provide adequate controller performance. To implement gain-scheduled MPC, first design a model predictive controller for each operating point, and then design a scheduling signal that switches the controllers at run time. To reduce online computational effort, you can also implement gain-scheduled explicit MPC in Simulink®. For more information, see Gain-Scheduled MPC.
mpcmoveMultiple | Compute gain-scheduling MPC control action at a single time instant |
mpcmoveopt | Option set for mpcmove function |
mpcstate | MPC controller state |
| Multiple MPC Controllers | Simulate switching between multiple implicit MPC controllers |
| Multiple Explicit MPC Controllers | Multiple explicit MPC controllers |
Control a nonlinear plant over a wide range of operating conditions by switching between a predefined set of MPC controllers in a coordinated fashion.
Schedule Controllers at Multiple Operating Points
Control a nonlinear system by designing multiple MPC controllers for different plant operating conditions.
Gain-Scheduled MPC Control of Nonlinear Chemical Reactor
Control a nonlinear chemical reactor using a gain-scheduled model predictive controller as the reactor transitions from one operating condition to another.
Gain-Scheduled Implicit and Explicit MPC Control of Mass-Spring System
Implement gain-scheduled MPC control of a nonlinear plant using the Multiple MPC Controllers block and Multiple Explicit MPC Controllers block.
Gain-Scheduled MPC Control of an Inverted Pendulum on a Cart
Control an inverted pendulum in an unstable equilibrium position using a gain-scheduled model predictive controller.