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gain-scheduled control of nonlinear plants by switching controllers at run time

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 .

functions

	compute gain-scheduling mpc control action at a single time instant
	option set for `mpcmove` function
	mpc controller state

blocks

	simulate switching between multiple implicit mpc controllers
	multiple explicit mpc controllers

topics

gain-scheduled mpc basics

control a nonlinear plant over a wide range of operating conditions by switching between a predefined set of mpc controllers in a coordinated fashion.
control a nonlinear system by designing multiple mpc controllers for different plant operating conditions.

case studies

control a nonlinear chemical reactor using a gain-scheduled model predictive controller as the reactor transitions from one operating condition to another.
implement gain-scheduled mpc control of a nonlinear plant using the multiple mpc controllers block and multiple explicit mpc controllers block.
control an inverted pendulum in an unstable equilibrium position using a gain-scheduled model predictive controller.