Feasible parallel-update distributed MPC for uncertain linear systems sharing convex constraints

Trodden, Paul

doi:10.1016/j.sysconle.2014.08.012

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…Several approaches exist in the literature (Richards & How, 2007;Trodden, 2014;Trodden & Richards, 2010 but their performances depend on the choice of initial states and the ordering of systems used in the sequential optimization process. Among them, the single-update DMPC (SDMPC) approach (Trodden, 2014) appears to be less sensitive to initial state variations since an initialization process is used and is therefore chosen. Note that SDMPC optimizes only one agent at each time step, leaving the other agents to follow their respective predicted controls.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The method of Richards and How (2007) ensures the satisfaction of (3) using a sequential process: one system is optimized at a time while all others stay constant; this is followed sequentially by another system so that all M systems are optimized once in M time steps. Another approach is known as the cooperative MPC method (Trodden, 2014;Trodden & Richards, 2010. While specific details vary, the basic idea is that all systems within a cooperating set (possibly a singleton) are optimized jointly (or in parallel) while systems outside the cooperating set follow their predicted states and predicted controls.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

Wang

Ong

2017

Automatica

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

Wang

Ong

2017

Automatica

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“…In [123], a DMPC algorithm is developed for the dynamically decoupled MAS with coupled constraints; only one agent calculates the locally optimal control inputs and updates the system states at each time instant. As reported in [145,147], all agents with coupled constraints are optimized jointly in a cooperative set, which may have a strong requirement on the communication networks. Another direction for coupled constraints [158,159] that achieves global optimality is to distributively solve the dual optimization problem, where dual variables related to the coupled constraints are treated as consensus variables in the distributed optimization problem.…”

Section: Dmpc With Coupled Constraintsmentioning

confidence: 99%

Self-Triggered Min–Max DMPC for Asynchronous Multiagent Systems With Communication Delays

Wei

Zhang

Shi

2022

IEEE Trans. Ind. Inf.

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Distributed coordination of multi-agent systems (MASs) has been widely studied in various emerging engineering applications, including connected vehicles, wireless networks, smart grids, and cyber-physical systems. In these contexts, agents make the decision locally, relying on the interaction with their immediate neighbors over the connected communication networks. The study of distributed coordination for the multi-agent system (MAS) with constraints is significant yet challenging, especially in terms of ubiquitous uncertainties, the heavy communication burden, and communication delays, to name a few. Hence, it is desirable to develop distributed algorithms for the constrained MAS with these practical issues. In this dissertation, we develop the theoretical results on robust distributed model predictive control (DMPC) algorithms for two types of control problems (i.e., formation stabilization problem and consensus problem) of the constrained and uncertain MAS and apply robust DMPC algorithms in applications of cooperative marine vehicles.More precisely, Chapter 1 provides a systematic literature review, where the state-of-the-art DMPC for formation stabilization and consensus, robust MPC, and MPC for motion control of marine vehicles are introduced. Chapter 2 introduces some notations, necessary definitions, and some preliminaries. In Chapter 3, we study the formation stabilization problem of the nonlinear constrained MAS with uncertainties and bounded time-varying communication delays. We develop a min-max DMPC algorithm with the self-triggered mechanism, which significantly reduces the communication burden while ensuring closed-loop stability and robustness. Chapter 4 investigates the consensus problem of the general linear MAS with input constraints and bounded time-varying delays. We design a robust DMPC-based consensus protocol that integrates a predesigned consensus protocol with online DMPC optimization techniques. Under mild technical assumptions, the estimation errors propagated over prediction due to delay-induced inaccurate neighboring information are proved bounded, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying control input constraints. Chapter 5 proposes a Lyapunov-based DMPC approach for the formation tracking control problem of cooperative autonomous underwater vehicles (AUVs) subject to environmental disturbances. A stability constraint leveraging the extended state observer-based auxiliary control law and the associated Lyapunov function is incorporated into the optimization problem to enforce the stability and enhance formation tracking performance. A iv collision-avoidance cost is designed and employed in the DMPC optimization problem to further guarantee the safety of AUVs. Chapter 6 presents a tube-based DMPC approach for the platoon control problem of a group of heterogeneous autonomous surface vehicles (ASVs) with input constraints and disturbances. In particular, a coupled inter-vehicle safety constraint is added to the DMPC ...

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“…Digital Object Identifier 10.1109/TAC.2020.3021528 been reported in [4], allowing subsystems to carry out optimizations simultaneously. However, the global optimality of the overall system is not necessarily guaranteed under these methods.…”

Section: Introductionmentioning

confidence: 99%

Distributed Model Predictive Control and Optimization for Linear Systems With Global Constraints and Time-Varying Communication

Jin

Yan

et al. 2021

IEEE Trans. Automat. Contr.

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In the article, we study the distributed model predictive control (DMPC) problem for a network of linear discrete-time systems, where the system dynamics are decoupled, the system constraints are coupled, and the communication networks are described by time-varying directed graphs. A novel distributed optimization algorithm called the push-sum dual gradient (PSDG) algorithm is proposed to solve the dual problem of the DMPC optimization problem in a fully distributed way. We prove that the sequences of the primal, and dual variables converge to their optimal values. Furthermore, to solve the implementation issues, stopping criteria are designed to allow early termination of the PSDG Algorithm, and the gossip-based push-sum algorithm is proposed to check the stopping criteria in a distributed manner. It is shown that the optimization problem is iteratively feasible, and the closed-loop system is exponentially stable. Finally, the effectiveness of the proposed DMPC approach is verified via an example.Index Terms-Distributed model predictive control (DMPC), global constraints, gossip-based push-sum algorithm, push-sum dual gradient (PSDG) algorithm, time-varying directed graphs.

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Feasible parallel-update distributed MPC for uncertain linear systems sharing convex constraints

Cited by 14 publications

References 21 publications

Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

Self-Triggered Min–Max DMPC for Asynchronous Multiagent Systems With Communication Delays

Distributed Model Predictive Control and Optimization for Linear Systems With Global Constraints and Time-Varying Communication

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