Computationally efficient infinite-horizon indefinite model predictive control with disturbance preview information

This paper proposes a model predictive control (MPC) scheme for maximising the benefit of a useful disturbance by exploiting preview information of the disturbance, in the context of goal-oriented operation. For a constrained system, subject to a persistent, bounded, and predictable disturbance, rather than attenuating the influence of disturbance, the proposed MPC aims to utilise the disturbance to optimise high-level economic criteria, e.g., profitability and productivity, which are normally represented by an indefinite cost function. For linear time-invariant systems, after examining the influence of the future disturbance profile, a computationally efficient finite-horizon convex approach is proposed to approximate the solution of the original possibly non-convex infinite-horizon optimisation problem. Then, a receding-horizon implementation is developed, taking into account the recursively updated disturbance prediction, and the recursive feasibility and input-to-state stability of the implementation are established. Numerical examples are provided to verify the efficacy of the proposed method.