Cooperative distributed model predictive control,

Abstract In this paper we propose a cooperative distributed linear model predictive control strategy applicable to any finite number of subsystems with the following features: hard input constraints are satisfied; the distributed control provides nominal stability for the same set of plants as centralized control; terminating the iteration of the distributed controllers prior to convergence retains closed-loop stability; in the limit of iterating to convergence, the control is plantwide Pareto optimal and equivalent to the centralized control solution; no coordination layer is employed. We first prove exponential stability of suboptimal model predictive control and show 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

Networked cooperation-based distributed model predictive control using Laguerre functions for large-scale systems

International audienceThis paper proposes a novel cooperative distributed control system architecture based on unsupervised and independent Model Predictive Control (MPC) using discrete-time Laguerre functions to improve the performance of the whole system. In this distributed framework, local MPCs algorithms might exchange and require information from other sub-controllers via the communication network to achieve their task in a cooperative way. In order to reduce the computational burden in the local rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are used to approximate the predicted control trajectory. Simulation results show that the proposed architecture could guarantee satisfactory global performance even under strong interactions among the subsystems

Cooperative distributed MPC for tracking

This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

Aperiodic Communication for MPC in Autonomous Cooperative Landing

In this paper, we focus on the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). These heterogeneous agents with nonlinear dynamics are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on the Distributed Model Predictive Control (MPC). One of our main contributions is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm requires that agents update the rendezvous location only when they are not guaranteed to reach it. Therefore, the exchange of information occurs aperiodically and proposed algorithm improves the communication efficiency. Furthermore, we prove the recursive feasibility of the algorithm. The simulation results show the effectiveness of our algorithm applied to the problem of autonomous cooperative landing.Comment: 7 pages, 6 figures, This work has been submitted to IFAC for possible publication, 7th IFAC Conference on Nonlinear Model Predictive Control 202

Distributed Model Predictive Control

Distributed model predictive control refers to a class of predictive control architectures in which a number of local controllers manipulate a subset of inputs to control a subset of outputs (states) composing the overall system. Different levels of communication and (non)cooperation exist, although in general the most compelling properties can be established only for cooperative schemes, those in which all local controllers optimize local inputs to minimize the same plantwide objective function. Starting from state-feedback algorithms for constrained linear systems, extensions are discussed to cover output feedback, reference target tracking, and nonlinear systems. An outlook of future directions is finally presented