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robust model predictive control for greenhouse temperature based on particle swarm optimization

Information Processing in Agriculture 2018年第3期5卷 329-338页

作者： Lijun Chen Shangfeng Du Yaofeng He Meihui Liang Dan Xu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083PR China

Application of model predictive control(MPC)in horticultural practice requires detailed ***,even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded *** enforce robustness during the controller design stage,this paper proposes a particle swarm optimization(PSO)-based robust MPC strategy for greenhouse temperature *** strategy is based on a nonlinear physical temperature affine *** robust MPC technique requires online solution of a minimax optimal control problem,which optimizes the tradeoff between set point tracking and cost requirements *** minimax optimization problem is reformulated to a nonlinear programming problem with *** is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are *** results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables,higher control precision,and stronger robustness than the conventional MPC.

关键词： Greenhouse temperature robust model predictive control Particle swarm optimization Affine non-linear systems

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A Linear Fractional Transformation Based Approach to robust model predictive control Design in Uncertain Systems

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IEEE ACCESS 2020年 8卷 220215-220226页

作者： Ghaffari, Valiollah Mobayen, Saleh Wong, Wing-Kwong Persian Gulf Univ Fac Intelligent Syst Engn & Data Sci Dept Elect Engn Bushehr *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Natl Yunlin Univ Sci & Technol Dept Elect Engn Touliu 64002 Yunlin Taiwan

A novel robust model predictive control (RMPC) scheme is developed for uncertain nonlinear systems. To the RMPC design, firstly, the uncertain system would be described using a linear fractional transformation (LFT). Then, regarding the system's uncertainties and control limitations, a linear matrix inequality (LMI) based control strategy is addressed to translate the RMPC synthesis into a minimization problem. Thus the controller's gains are automatically updated at some time-instants by the solution of such optimization problem. Finally, the outcomes are numerically applied in some control examples. The simulation results show the effectiveness of the suggested robust predictive controller in comparison to similar RMPC techniques.

关键词： Uncertain systems Uncertainty Linear matrix inequalities predictive control Optimization Nonlinear systems Mathematical model Linear fractional transformation linear matrix inequality robust model predictive control uncertain systems

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Approximate explicit robust model predictive control of a CSTR with fast reactions

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CHEMICAL PAPERS 2019年第3期73卷 611-618页

作者： Bakarac, Peter Kvasnica, Michal Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Fac Chem & Food Technol Radlinskeho 9 Bratislava 81237 Slovakia

This paper presents a novel approach to the construction of explicit representation of robust model predictive feedback laws for constrained linear systems with parametric uncertainties. The approach is based on employing approximate dynamic programming where a large optimal control problem is split into a sequence of problems of smaller size. To be able to solve each such problem explicitly, we employ a bounded approximation of optimal value functions. Such a procedure is demonstrated to be superior, both in terms of construction time as well as in the complexity of the resulting controllers, to traditional approaches based on the one-shot approach. The proposed method is demonstrated on a simulation case study involving a continuous stirred tank reactor (CSTR) where a fast multi-component chemical reaction takes place.

关键词： Explicit model predictive control robust model predictive control Parametric uncertainties Continuous stirred tank reactor

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Transient Performance of Tube-based robust Economic model predictive control 7

Transient Performance of Tube-based Robust Economic Model Pr...

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7th IFAC Conference on Nonlinear model predictive control (NMPC)

作者： Kloeppelt, Christian Schwenkel, Lukas Allgoewer, Frank Mueller, Matthias A. Leibniz Univ Hannover Inst Automat Control Hannover Germany Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany

In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes. In particular, we consider both tube-based MPC schemes with and without terminal conditions. We show that the closed-loop performance obtained by applying such MPC schemes is approximately optimal when evaluated both on finite and infinite time horizons. These performance bounds are similar to those derived previously for nominal economic MPC. The theoretical results are discussed in a numerical example. Copyright (C) 2021 The Authors.

关键词： Economic predictive control robust model predictive control

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robust model predictive control with Scenarios for Aggregators in Grids with High Penetration of Renewable Energy Sources.

Robust Model Predictive Control with Scenarios for Aggregato...

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IEEE International Conference on Communications, control, and Computing Technologies for Smart Grids (SmartGridComm)

作者： Parvizi, Jacopo Jorgensen, John B. Madsen, Henrik Danish Tech Univ Dept Comp Sci & Appl Math Lyngby Denmark

ISBN: (纸本)9781538679548

Integrating flexible consumers in grids with high penetration of renewable energy sources requires a robust power balancing strategy. The methodologies and solutions suggested in this article aim to describe a flexible framework for controlling future electric energy systems by formulating the aggregation problem as a hierarchical robust optimization problem on different aggregation levels. The Aggregator solves a minmax robust optimization problem through a model predictive control framework. With two numercal examples we show how our algorithm controls flexible loads in closed loop, such that consumption follows the stochastic changing production influenced by the penetration of renewables into the power system.

关键词： robust model predictive control renewable energy sources Aggregator direct control indirect control DER

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Waypoint Tracking via Tube-based robust model predictive control for Crop Monitoring with Fixed-Wing UAVs

Waypoint Tracking via Tube-based Robust Model Predictive Con...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Mammarella, Martina Ristorto, Gianluca Capello, Elisa Bloise, Nicoletta Guglieri, Giorgio Dabbene, Fabrizio Natl Res Council Italy Inst Elect Comp & Telecommun Engn Turin Italy Univ Torino Dipartimento Sci Agr Forestall & Alimentari Turin Italy MAVTech Srl NOI Techpk Sudtirol Alto AdigeVia Ipazia 2 I-39100 Bolzano Italy Politecn Torino Dept Mech & Aerosp Engn Turin Italy Politecn Torino PIC4SeR Politecn Interdept Ctr Serv Robot Turin Italy

ISBN: (纸本)9781728136110

Crop monitoring and farm activities with innovative systems, as Unmanned Aerial Vehicles, is an undergoing research known as fourth agricultural revolution. In our paper. two different farming scenarios are proposed, in which a trajectory tracking based on model predictive control is proposed in combination with a waypoint-based guidance algorithm. This Guidance and control algorithm is also combined with an on-board sensor to characterize the measured data. Simulations are performed to show the effectiveness of the proposed control scheme.

关键词： Crop Monitoring Precision Farming Unmanned Aerial Vehicle robust model predictive control

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robust constrained model predictive control design for piecewise non-linear systems with multiple operating points

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2020年第6期42卷 1110-1121页

作者： Shokrollahi, Ali Shamaghdari, Saeed Iran Univ Sci & Technol Sch Elect Engn Tehran *** Iran

In this paper, a robust model predictive control (MPC) scheme is developed for non-linear systems. We propose a new modeling approach, entitled piecewise non-linear, for plants with multiple operating points and with unstructured uncertainties. The systems, in each subregion, are composed of an affine model perturbed by an additive non-linear term which is locally Lipschitz. Considering a non-linear term in the model changes the control problem from a convex program to a non-convex one, which is much more challenging to solve. A standard dual-mode control strategy is introduced by parameterizing the infinite horizon control moves into a number of free control moves followed by a single state feedback law. The designed controller is robust against model uncertainty and guarantees system stability under switching between subregions. Numerical examples on a highly non-linear chemical process and another non-linear system are used to evaluate the applicability of the proposed method. Simulation results show a better performance in terms of speed of convergence and feasibility compared with the conventional robust MPC designs.

关键词： robust model predictive control piecewise non-linear multiple operating point Lipschitz non-linear system linear matrix inequality

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robust model predictive control for Linear Discrete-Time System With Saturated Inputs and Randomly Occurring Uncertainties

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ASIAN JOURNAL OF control 2018年第1期20卷 425-436页

作者： Wang, Jianhua Song, Yan Zhang, Sunjie Liu, Shuai Dobaie, Abdullah M. Univ Shanghai Sci & Technol Key Lab Modern Opt Syst Dept Control Sci & Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia

This paper investigates the robust model predictive control (RMPC) problem for a class of linear discrete-time systems subject to saturated inputs and randomly occurring uncertainties (ROUs). Due to limited bandwidth of the network channels, the networked transmission would inevitably lead to incomplete measurements and subsequently unavoidable network-induced phenomenon that include saturated inputs as a special case. The saturated inputs are assumed to be sector-bounded in the underlying system. In addition, the ROUs are taken into account to reflect the difficulties in precise system modelling, where the norm-bounded uncertainties are governed by certain uncorrelated Bernoulli-distributed white noise sequences with known conditional probabilities. Based on the invariant set theory, a sufficient condition is derived to guarantee the robust stability in the mean-square sense of the closed-loop system. By employing the convex optimization technique, the controller gain is obtained by solving an optimization problem with some inequality constraints. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed RMPC scheme.

关键词： Linear discrete-time system saturated inputs randomly occurring uncertainties robust model predictive control

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robust model predictive control of the automatic operation boats for aquaculture

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年第PartA期142卷 118-125页

作者： Zhang, Jun Sun, Tairen Zhao, Dean Hong, Jianqing Sun, Yueping Jiangsu Univ Sch Elect & Informat Zhenjiang 212013 Peoples R China

This paper proposes a robust model predictive control (RMPC) approach for the automatic operation boats to cast baits evenly along desired paths. The difficulties in the control design come from the control system model, which is nonlinear, underactuated, input saturated, and disturbed by time-varying signals. The RMPC overcomes these difficulties by the receding horizon optimization explicitly considering the input saturation and using the mixed H-2/H-infinity cost function. To decrease computational complexity of the RMPC, a polyhedral model is constructed as the predictive model based on dynamics of the path-following error. The feasibility and effectiveness of the proposed path-following control is verified by theoretical analysis and illustrated by simulations and experiments. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Automatic operation boat Straight path following Underactuated system Polyhedral model robust model predictive control

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Transient Performance of Tube-based robust Economic model predictive control ⁎

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IFAC-PapersOnLine 2021年第6期54卷 28-35页

作者： Christian Klöppelt Lukas Schwenkel Frank Allgöwer Matthias A. Müller Institute of Automatic Control Leibniz University Hannover Germany Institute for Systems Theory and Automatic Control University of Stuttgart Germany

关键词： Economic predictive control robust model predictive control

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