Model predictive control is an increasingly-popular modern control technique. The idea is that the current control action is computed on-line, and at every sampling instant, by solving a finite-horizon optimization problem, formulated from a model of system dynamics, constraints, and cost function. The optimization framework means that the technique is - contrary to most control methods - particularly adept at handling hard constraints, which arise in many applications.
This talk will first provide an overview of the underlying framework and introduce some theoretical concepts, before presenting recent work on applying forms of MPC to non-traditional applications, including vehicle path planning and spacecraft rendezvous.
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