An efficient cooperative-distributed model predictive controller with stability and feasibility guarantees for constrained linear systems

Santana, Daniel D.; Martins, Márcio A. F.; Odloak, Darci

doi:10.1016/j.sysconle.2020.104701

Cited by 14 publications

(5 citation statements)

References 15 publications

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“…Model Predictive Control, is an iterative finite horizon control strategy, based on an optimization problem of a difinite plant model (Santana et al, 2020). Its main task is that it allows a cost function calculation to obtain the performances of the controller in the future based on the current real or estimated plant state x k and a serie of future inputs u k at each discrete sampling time (k).…”

Section: Brief Remainder Of Constrainded (Mpc) and Optimization Problem Principlesmentioning

confidence: 99%

Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

et al. 2021

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In this paper, we present a Constrained Discete Model Predictive Control (CDMPC) strategy application for relative humidity control. In this sense, and for our system inside humidity dynamics description, a green-house prototype is engaged and a state space form which fits properly a set of collected data of the greenhouse humidity dynamics is presented as mathematical model. This latest is used for the CDMPC starategy application, which purpose is to select the best control moves based on an optimization procedure regarding the constraints on the control. By the means of Matlab/ Simulink and Yalmip toolbox algorithms, numerical simulations were held to proove the effectiveness of the controller, garanteeing both the constraints feasibility and system stability.

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Section: Brief Remainder Of Constrainded (Mpc) and Optimization Problem Principlesmentioning

confidence: 99%

Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

et al. 2021

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“…A distributed noncooperative MPC was proposed in [20], where input and state constraints are considered and convergence of the closed-loop control system is proved. According to the optimization problem and the types of information exchange among subsystems, the distributed MPCs are often classified as cooperative [21][22][23] and noncooperative [19,20,24]. In cooperative distributed MPC, all subsystems solve a global optimization problem and exchange information with each other.…”

Section: Introductionmentioning

confidence: 99%

Distributed Double-Layered Dynamic Matrix Control for Large-Scale System

Wang

Cai

Xin

2022

Mathematical Problems in Engineering

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A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsystems. The distributed open-loop prediction equation of each subsystem is formed based on the predicted output of each local subsystem and effects of its interconnecting neighbor subsystems. Due to simultaneous optimization, at each prediction, the coupling effects of neighbor subsystems are not available in time. Thus, the assumed value is utilized instead. In the economic optimization stage, conflicts may occur among different economic optimization goals. Utopia-tracking strategy is introduced to optimize multiple steady-state targets. Then, the obtained steady-state target values are taken as reference values and tracked in subsequent dynamic control. The actual control move for each subsystem is finally calculated. The proposed algorithm is tested on Shell heavy oil fractionator benchmark, and the effectiveness is demonstrated by comparing with the typical double-layered dynamic matrix control algorithm.

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“…Although these methods mentioned above have disposed of the uncertainty problem of the formation control with MPC, they are not combined with a distributed control. As for the MPC applied in the distributed control (distributed model predictive control (DMPC)), a cooperative DMPC algorithm was reported [23] where the relaxed constraints are used to find the optimization problem including the global optimization objective. is algorithm can be utilized to control the UAV tracking the ground target for real-time path planning [24] and multiple cooperative autonomous ships forming a multiship formation system [25].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

Shang

Zhu

Pan

et al. 2021

Discrete Dynamics in Nature and Society

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In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. The robust performance objective minimization is applied to replace the quadratic performance objective minimization to establish the optimization problem, where the model uncertainty is considered in the distributed system. The control policy is derived by applying the data-driven strategy, and the future predictive value is obtained by employing the linear law in the historical data. Lyapunov theory is referred to analyze the stability of the mobile robot formation system. The effectiveness of the proposed method is proved by a set of simulation experiments.

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An efficient cooperative-distributed model predictive controller with stability and feasibility guarantees for constrained linear systems

Cited by 14 publications

References 15 publications

Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

Distributed Double-Layered Dynamic Matrix Control for Large-Scale System

A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

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