Stochastic Distributed Predictive Tracking Control Under Inexact Minimization

Due to cooperative distributed model predictive control scheme is widely applied in large scale networks of systems, so asymptotic stability is a very important index for measuring the performance of cooperative distributed model predictive control. Based on the obvious inequality from classical Lyapunov stability condition, we derive a set of linear matrix inequalities to replace the common inequalities by using Schur complement and S-procedure. Furthermore when combining local state and input constraint sets, a set of more complex linear matrix inequalities is used to guarantee the asymptotically stable for cooperative distributed model predictive control.

Partitioned Based Cooperative Distributed Model Predictive Control for Large-Scale Nonlinear Systems

A tube-based distributed model predictive control (DMPC) scheme is proposed for dynamically coupled linear systems. The control scheme is designed to guarantee local performance even when neighboring controllers are not complying with the requirements of the algorithm (e.g., they are malicious or faulty). The resulting conservativeness is minimized, for controllers aim to minimize their state and input constraint sets to reduce mutual disturbances. Also, sufficient conditions for feasibility and exponential stability are given. Finally, these ideas are illustrated and assessed with respect to other robust DMPC via a simulated example.

Using aggregated heterogeneous thermostatically controlled loads (TCLs) to provide ancillary services in a smart grid is a promising technique. This paper proposes an improved population model with lockout time considered to describe the aggregate dynamics. Based on that, a distributed model predictive control (DMPC) scheme is investigated to control a population of TCLs for the regulation service. Full derivations of the system model and the DMPC scheme are provided. A flexibility penalty is developed and added to the objective function to preserve the regulation flexibility. Simulation results, tested by real data, reveal the feasibility and effectiveness of the proposed method.

This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. The formation control experiments showcase the flexibility of the approach on scenarios with point-to-point transitions, trajectory tracking, collision avoidance, and moving obstacles.

SummaryIn this paper, a distributed model predictive control is proposed to control Lipschitz nonlinear systems. The cooperative distributed scheme is considered where a global infinite horizon objective function is optimized for each subsystem, exploiting the state and input information of other subsystems. Thus, each control law is obtained separately as a state feedback of all system's states by solving a set of linear matrix inequalities. Due to convexity of the design, convergence properties at each iteration are established. Additionally, the proposed algorithm is modified to optimize only one control input at a time, which leads to a further reduction in the computation load. Finally, two application cases are studied to show the effectiveness of the proposed method.

In this paper, we present a distributed model predictive control strategy for discrete-time interconnected linear systems. The overall system is composed of a number of discrete-time interconnected linear subsystems. The MPC controllers of subsystems communicate their information to handle the interconnection effects. It is known that distributed MPC relies on communication networks to transmit and share information among subsystems. Communication delays, resulting in delayed information exchange among subsystems, may inhibit the use of any distributed MPC approach. To address this problem, a delay handling mechanism is employed. Besides, it is showed that the proposed method guarantees input-to-state stability characterization for both local subsystems and the global system under some predetermined assumptions. The simulation results are exploited to illustrate the effectiveness of the proposed method.

Cooperative distributed model predictive control

Game Theory Based Distributed Model Predictive Control for a Hydro-Power Valley Control

This brief proposes a distributed model predictive control scheme based on satisficing theory in which the relative importance of each subsystem changes dynamically according to the current conditions. The controllers implement a situational altruism, where less satisfied controllers will automatically receive more importance. The global cost, instead of being fix as in other approaches, emerges from the cooperation process. The proposed control scheme guarantees both that the control input is Pareto-optimal and that each controller is guaranteed a minimum degree of optimality, even under coupled constraints. The main properties of the proposed approach are demonstrated through a traffic control example.

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

This paper deals with the distributed model predictive control problem in multi-agent systems under fixed interaction topologies. we propose a distributed model predictive control schemes with guaranteed closed-loop stability. In this distributed structure, the problem is decomposed into a set of distributed optimal control problems. Simulation results show that the proposed scheme is effective under fixed interaction topologies among subsystems.

A distributed model predictive control strategy for finite-time synchronization problems in multi-agent double-integrator systems

In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme, which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.

In order to reduce the heavy communication burden of cooperative distributed model predictive control (DMPC), A DMPC algorithm based on event-trigger is proposed to balancing the control performance and communication burden through optimizing the interconnection structure of subsystems. An event-trigger using the system Lyapunov function is constructed to avoid solving the introduced MINLP optimization directly, the optimal interconnection structure will be updated only when the event-trigger is triggered. The feasibility of the proposed algorithm in system performance and communication burden decrease is validated by the load frequency control (LFC) problem simulation of interconnected power system.