Abstract-A new distributed MPC algorithm for the regulation of dynamically coupled subsystems is presented in this paper. At each time instant, the control action is computed via two robust controllers working in a nested fashion. The inner controller provides local reference trajectories computed on a fully decentralized framework. The outer controller uses this information to take into account the effects of the dynamic coupling and implement a distributed control action. The tube-based approach to robustness is employed. A supplementary constraint is included in the outer optimization problem to provide recursive feasibility of the overall controller.

Abstract-This paper presents a new approach to deal with the dual problem of system identification and regulation. The main feature consists of breaking the control input to the system into a regulator part and a persistently exciting part. The former is used to regulate the plant using a robust MPC formulation, in which the latter is treated as a bounded additive disturbance. The identification process is executed by a simple recursive least squares algorithm. In order to guarantee sufficient excitation for the identification, an additional nonconvex constraint is enforced over the persistently exciting part.

Abstract:We propose a distributed model predictive control approach for linear time-invariant systems coupled via dynamics. The proposed approach uses the tube MPC concept for robustness to handle the disturbances induced by mutual interactions between subsystems; however, the main novelty here is to replace the conventional linear disturbance rejection controller with a second MPC controller, as is done in tube-based nonlinear MPC. In the distributed setting, this has the advantages that the disturbance rejection controller is able to consider the plans of neighbours, and the reliance on explicit robust invariant sets is removed.

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A new adaptive predictive controller for constrained linear systems is presented. The main feature of the proposed controller is the partition of the input in two components. The first part is used to persistently excite the system, in order to guarantee accurate and convergent parameter estimates in a deterministic framework. An MPC-inspired receding horizon optimization problem is developed to achieve the required excitation in a manner that is optimal for the plant. The remaining control action is employed by a conventional tube MPC controller to regulate the plant in the presence of parametric uncertainty and the excitation generated for estimation purposes. Constraint satisfaction, robust exponential stability, and convergence of the estimates are guaranteed under design conditions mildly more demanding than that of standard MPC implementations.