For a class of networked systems, this article proposed a distributed model predictive control (DMPC) design where the actual control law is composed of the solution of an independent optimization problem and a neighborhood states feedback part. By the proposed method, each subsystem‐based MPC is designed in a distributed way and the neighborhood feedback provides more potential for the designed DMPC to be applied to a wider class of systems. First, an LMI optimization problem is designed for determining the parameters of the neighborhood feedback matrix and corresponding invariant sets where the information of neighboring subsystems is explicitly involved. The designed feedback control law deals with the affection of the interacted subsystems and limits the deviation between the nominal model and the real system within the robust invariant set. Then, the independent suboptimal problem is developed using a predictive model without considering interconnections among subsystems, where the state is restricted by a tightened constraint which is related to the designed robust invariant set. The feasibility and stability of the closed‐loop system are analyzed. The proposed DMPC enables the direct removal or plugging‐in of subsystems. Simulation results demonstrated the effectiveness of the method.

In this paper, we integrate the blueonline-updating Gaussian Process (GP) models into the Model Predictive Control (MPC) design of nonlinear systems with time-varying dynamics to obtain a more accurate data-based prediction of state, and more relaxed constraints. Specifically, considering that the GP inferred from the Bayesian framework provides both the predictive value and the estimation of uncertainties, a mechanism of updating the predictive model and the stability constraint based on the Lyapunov techniques is designed according to the online estimation of the mismatch between the prediction by GP and that of the real system. It keeps the feasibility of the designed MPC system. Besides, the stability of the closed-loop system is proven to be guaranteed under a certain probability. Applying the designed method to a chemical process shows the efficiency of the proposed data-driven MPC.

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