Cooperative distributed model predictive control
By B. T. Stewart, Aswin N. Venkat, James B. Rawlings, Stephen J. Wright, Gabriele Pannocchia. 2010
490 citations recorded by the source corpus.
Abstract In this paper we propose a cooperative distributed linear model predictive control strategy applicable to any finite number of subsystems satisfying a stabilizability condition. The control strategy has the following features: hard input constraints are satisfied; terminating the iteration of the distributed controllers prior to convergence retains closed-loop stability; in the limit of iterating to convergence, the control feedback is plantwide Pareto optimal and equivalent to the centralized control solution; no coordination layer is employed. We provide guidance in how to partition the subsystems within the plant. We first establish exponential stability of suboptimal model predictive control and show that the proposed cooperative control strategy is in this class. We also establish that under perturbation from a stable state estimator, the origin remains exponentially stable. For plants with sparsely coupled input constraints, we provide an extension in which the decision variable space of each suboptimization is augmented to achieve Pareto optimality. We conclude with a simple example showing the performance advantage of cooperative control compared to noncooperative and decentralized control strategies.
Subfields: Control and Systems Engineering
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In Control and Systems Engineering
Citations in the corpus
- An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, methodology): “functions, distributed optimization has also been considered for both infinite-horizon cost functions [247]–[251] given by Ji = R∞ 0 [X T(t)QX(t) + UT(t)RU(t)]dt and finite-horizon cost functions [252]–[255] given by Jf = Rt f 0 [XT(t)QX(t)+ UT(t)RU(t)]dt, where X ∈ Rn is the state, U ∈ Rn is the cont”
- Distributed Model Predictive Control of Switched Nonlinear Systems with Scheduled Mode Transitions (2013, unknown): “rJð~ ukÞðu k2~ ukÞ Jðu kÞ2Jð~ ukÞ DJðukÞ < 0 (24)”
- Model Predictive Control — Status and Challenges (2013, unknown): “分布式预测控制的研究重点包括各子 系统之间耦合关联的处理、子系统的优化决策及相 互间的信息交换机制、全局稳定性的保证及最优性 的评估等 [42] .”
- Distributed model predictive control: A tutorial review and future research directions (2013, unknown): “(5.2.2) converges to the one of the corresponding centralized control system (Bertsekas & Tsitsiklis, 1997; Christofides, Liu, & Mu˜noz de la Pe˜na, 2011; Stewart et al., 2010).”