An improved design of aggregation-based model predictive control

Abstract Linear aggregation in the input is an effective method to reduce the online computational burden of model predictive control (MPC) but at the cost of degradations in the closed-loop performance. In this paper, an improved aggregation-based MPC algorithm is developed to reduce these degradations. In this algorithm, a time-varying base vector is utilized in conjunction with the quasi-equivalent aggregation strategy. Furthermore, by relaxing the constraints with a sequence of reachable sets, a switching strategy is adopted to enlarge the attractive region of the resulting aggregation-based MPC.

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