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

In this paper, a synchronization problem for a group of agents described by double-integrator dynamics and time-varying communication network is formally addressed. The main aim consists in ensuring that the agents, organized as a swarm, are capable to synchronize in finite time the swarm centroid to a given reference trajectory. To this end, an ad-hoc distributed control architecture is developed by taking advantage from a novel swarm formation modelling and an hybrid model predictive control scheme.

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.

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.

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.

Toward Plant-wide Control of Reticulated Systems Arising in Alumina Refineries with Online Stabilisation

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.

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.

Distributed Model Predictive Control with Asymmetric Adaptive Terminal Sets for the Regulation of Large-scale Systems

This article presents a reconfigurable distributed model predictive control strategy based on dissipativity theory to deal with online process network topology change. We mainly address the situation when process units want to join or leave the process network during closed-loop operation, while maintaining the plant-wide stability. The capacity of capturing the effects of process network topology on plant-wide stability is one key feature of dissipative systems theory, which can be utilized to deal with the challenge of performing online process network reconfiguration. The redesign is restricted to subsystems whose input-output properties are directly influenced by the topology change to allow quick operation. When the request of process units joining or leaving the process network is sent, feasibility of the process network topology change has to be assessed first. If permitted, the controllers of the influenced subsystems have to be updated for the modified dynamics to maintain the stability constraint of the plant-wide system, which is translated into dissipative trajectory condition for each local controller. The main advantage of the proposed dissipativity based method is that the design procedure is simple and straightforward compared to some existing algorithm based on invariant set, in which a transition phase is required to be designed in advance to ensure recursive feasibility before process network topology change.

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

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.

In this article, we propose a chance-constrained cooperative distributed model predictive control scheme for reference tracking. We consider dynamically coupled linear time-invariant systems that are subject to local and coupling chance constraints. The proposed controller is based on the distributed alternating direction method of multipliers and is able to steer the system to any admissible setpoint while ensuring recursive feasibility under inexact dual optimization and unbounded additive disturbances. Under a central convex unimodality assumption, the chance constraints are guaranteed to hold in closed loop. This article closes with two numerical examples. The first one is of an academic nature in order to highlight the chance constraint satisfaction, convergence, and numerical properties of the proposed algorithm. The second one is a practical scenario of a benchmark process.