Distributed aperiodic model predictive control for multi‐agent systems

Abstract

In this study, the authors propose an aperiodic formulation of model predictive control for distributed agents with additive bounded disturbances. In the proposed method, each agent solves an optimal control problem only when certain control performances cannot be guaranteed according to certain triggering rules. This could lead to the reduction of energy consumption and the alleviation of over usage of communication resources. The triggering rules are derived for both event-triggered and self-triggered formulation. The authors proposed method is also verified through a simulation example.

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

“…Graph theoretic topology model [45][46][47][48][49][50][51] ·Simple model structure ·High redundancy and easy to expand ·Robustness is greatly affected by graph Non-cooperative dynamic game model [52][53][54] ·Each agent can achieve the optimal balanced state ·Algorithm is complex and time-consuming Genetic algorithm [55][56][57] ·High prediction accuracy ·Fast convergence ·Scalability and parallelism operation ·Most of the parameters depend on experience ·Slow dynamic response PSO algorithm [58][59][60][61] ·Simple model structure ·Fast computation speed ·Efficient economic scheduling ·Improve the frequency and voltage of MG ·Not handling the discrete optimization problems…”

“…Distributed multi-agent control method has been widely used to establish optimal model to enhance reliability and energy management, optimization, and improve the performance of ancillary services [44]. Various methods for system modelling, including topology models and mathematic models, in MAS have been reviewed in this paper, such as the graph topology model [45][46][47][48][49][50][51], non-cooperative game model [52][53][54], genetic algorithm [55][56][57] and particle swarm optimization (PSO) [58][59][60][61], etc. Moreover, consensus protocol is the rule of interaction among multiple agents in the complex systems, which describes the process of information interaction among agents and their neighbors.…”

Distributed model predictive control for unmanned aerial vehicles

“…Various strategies and approaches were studied for solving the motion coordination and formation control problems, which can be classified in three categories as it follows: 1) leaderfollower approach [15], [16], [17], [18], [19], 2) virtual structure approach [20], [21], 3) behavior based approach [22], [23], and [24]. Except from the above strategies, Model Predictive Control (MPC) [25], have been utilized for motion coordination and formation control problems [26], [27], [28] and [29]. In these approaches, MPC determined the optimal future control profile according to a prediction of the system behavior over a receding time horizon, while the control actions were computed repetitively by solving a constrained online optimal control problem, over a receding horizon, every time a state measurement or estimate became available.…”

Event-triggered intermittent sampling for nonlinear model predictive control

“…In ETMPC and STMPC, the OCPs are solved only when some events, generated based on certain control performance criteria, are triggered. These strategies have received an increased attention in recent years; most of the works focus on discrete-time systems, see e.g., [4,5,6,7,8,9,10,11], and some results include for the continuous-time case, see e.g., [12,13,14] for linear systems and [15,16,17,18,19,20] for nonlinear systems. In this paper, we are particularly interested in the case of nonlinear continuous-time systems.…”