Input-mapping based data-driven model predictive control for unknown linear systems with bounded disturbances

Abstract

The data-driven model predictive control (MPC) approach has been an effective tool for unknown constrained systems. However, most of the existing designs rely on the prior collected data sequence through offline trials, which may be affected by time-varying disturbances, or the online estimated system model with the non-trivial computation cost. This limits the applications of these designs. To alleviate these restrictions, in this paper, an input-mapping data-driven scheme is developed. This scheme online directly maps the future control policy and the predicted state to the past online noisy input/state data which are updated once new data come. This overcomes the limitations of previous designs. To ensure the system constraints, this scheme is combined with a tube MPC method and the proposed input-mapping data-driven tube MPC can guarantee the recursive feasibility and the input-to state stability. Two examples illustrate the advantages of the proposed method.