Model predictive control
Model predictive control is control action based on a prediction of the system output a number of time steps into the future. Originated from chemical process engineering, model predictive control has found its way into virtually all areas of control engineering. Our research focuses on the development of a general formulation of predictive control that subsumes both the input-output and state-space perspectives. We seek comprehensive answers to questions such as: What is the simplest way to justify the existence and structures of various input-output predictive models? How does one arrive at an input-output controller if the starting point of the derivation is a state-space model? Can explicit state-space model identification be avoided? What is an efficient strategy to synthesize a predictive controller from input-output data directly without having to resort to model identification? What is the role of predictive control in the disturbance rejection problem? How can we design model predictive controllers for a swarm of robots?
Faculty contact: Minh Q. Phan