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IEEE Annual International Energy Conversion Congress and Exhibition

作者： Males Tomlinson Toit Mouton Ralph Kennel Peter Stolze Universiteit van Stellenbosch Technische Universitat Munchen

ISBN: (纸本)9781479904839

This paper presents a novel finite-set model predictive control technique with a fixed switching frequency for a 5-level FCC converter. Standard implementations of finite-set model predictive control (FS-MPC) are typically characterised by a variable switching frequency, a high noise floor and a large voltage and current ripple. A method is proposed by selecting a fixed switching period and dividing it into smaller evaluation steps. The switching behaviour is obtained by using the analogy of PWM with triangular carriers to define regions during which only specific switching transitions are allowed. To account for the repetitive nature of the desired switching waveform, an average receding horizon prediction method is used. An anticipated prediction is made by calculating the average value for one switching period ahead, according to the position and state in the current switching period. The control method was practically implemented and evaluated, proved to be feasible and provided satisfactory results.

关键词： Fixed switching cycle Switching frequency model predictive control predictive control Converters distributed model predictive control nonlinear model predictive control control Methods Pulse width modulation Pokeweed Mitogens

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Exploiting Parallelization in Explicit model predictive control

Exploiting Parallelization in Explicit Model Predictive Cont...

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International Conference on Information, Communication and Automation Technologies

作者： Alessandro Zanarini Mahmoud Jafargholi Helfried Peyrl ABB Corporate Research

ISBN: (纸本)9781479904297

Traditionally model predictive control (MPC) has been mainly restricted to processes with rather slow dynamics and with sampling times ranging from a few minutes to hours, such as the ones encountered in the areas of (petro)chemicals, minerals and metals. However, recent algorithmic advances (such as the explicit approach for MPC) allowed the application of MPC to problems arising in the automotive or power electronics industry where the time scales are in the millior even the microsecond area. In this study we aim to push the limit of explicit MPC even further by exploiting the computational power offered by parallel CPU architectures. We present the parallelisation of three different algorithms and we report experimental results showing how for certain problems, the parallelisation offers performances that top state-of-the-art approaches.

关键词： Maximum Allowable Concentration model predictive control predictive control parallelization Explicit distributed model predictive control nonlinear model predictive control computational power time scales automotive engineering regional geography

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Cooperative distributed MPC using the Alternating Direction Multiplier Method

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IFAC Proceedings Volumes 2012年第15期45卷 445-450页

作者： Markus Kögel Rolf Findeisen Institute for Automation Engineering Otto-von-Guericke-University Magdeburg Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

We consider cooperative distributed model predictive control of a linear, time-invariant, discrete-time plant, which consists of coupled subsystems. The cooperating controllers minimize a quadratic cost criterion subject to input and state constraints. We outline two approaches for such distributed controllers using the alternating direction multiplier method, which allows to obtain convergence guarantees. We outline suitable stability criterions and illustrate the applicability and performance of the proposed methods by a simple example.

关键词： distributed model predictive control Alternating direction multiplier method

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A Unified Approach to Plant-wide Dissipative model predictive control *

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IFAC Proceedings Volumes 2012年第15期45卷 420-425页

作者： Michael James Tippett Jie Bao School of Chemical Engineering The University of New South Wales Sydney NSW 2052 Australia

An approach to plant-wide model predictive control based on dissipativity theory is presented. Dissipativity theory facilitates the decomposition of the plant-wide system into interacting process and controller networks. The controller may take a decentralized, distributed or fully centralized structure without significantly changing the complexity of analysis. This approach to dissipativity based control is enabled by the use of dynamic supply rates as quadratic difference forms which capture more dynamic system information than constant supply rates and decrease the conservativeness of the approach. Plant-wide dissipativity properties ensure stability of the plant-wide system and minimum performance.

关键词： distributed model predictive control Plant-wide control Dissipativity

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model predictive control for energy management in buildings Part 1: Zone model predictive control

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IFAC Proceedings Volumes 2012年第17期45卷 21-26页

作者： M.Y. Lamoudi M. Alamir P. Béguery Schneider-Electric Industries. 37 quai Paul Louis Merlin-38000 Grenoble-France CNRS-University of Grenoble Gipsa-lab BP 46 Domaine Universitaire 38400 Saint Martin d'Hères France

In this paper, a distributed model predictive control is proposed to manage the whole set of actuators (heating/cooling, ventilation, lighting, shading) in a multi-zone building to control comfort parameters (temperature, indoor CO 2 level and indoor illuminance). The control process is performed in a distributed fashion and handles variable prices as well as resource limitation in a context of a multi energy source building. To this end, we firstly present a zonal nonlinear model predictive controller which is concerned by zonal decision making -part 1 of the paper-.We then provide a coordination scheme based on a primal decomposition to address the resource allocation problem which occurs in the presence of global constraints on power consumption and/or global shared storage capability in the building -part 2 of the paper-Lamoudi et al. [2012]. We finally provide some simulation results attesting the fast convergence of control algorithm and the benefit of the controller.

关键词： distributed model predictive control Constrained control Building Management System Smart buildings

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Robust-distributed MPC with robust observer to handle communication loss

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IFAC Proceedings Volumes 2012年第15期45卷 280-285页

作者： Divya Kumar Walid Al-Gherwi Hector Budman Chemical Engineering Department University of Waterloo Waterloo ON N2L3G1 (Tel: 519-888-4567) Sanofi Pasteur Toronto ON M2R3T4 (Tel:416-667-2955)

distributed model predictive control (DMPC) algorithms reported in the literature had often assumed that state measurements are available to all subsystems at all time instants. However, this assumption is not valid in the presence of loss of communication or data. Then designing for such losses within DMPC becomes critical to overall performance and stability of the system. In this work communication loss was considered for robust DMPC by using within each subsystem an observer, which continuously and recursively estimates states' bounds for all the plant states. These state bounds are then posed as extra constraints within a Linear Matrix Inequalities' formulation that is used to calculate the control actions based on the minimization of an upper bound on robust performance.

关键词： distributed model predictive control Robustness Linear Matrix Inequalities

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Laboratory Plant of a Gas Distribution Network

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IFAC Proceedings Volumes 2012年第11期45卷 390-395页

作者： D. Navia R. Martí D. Sarabia C. de Prada Systems Engineering and Automatic Control Department University of Valladolid Spain

This work shows the implementation of a laboratory – scale oxygen distribution plant built with the aim of being used as a benchmark for control and optimization of networked processes. The plant emulates the architecture and the main problems that can be found in real facilities which combine several complex processes linked with shared resources that impose the need of coordinated operation and control. The paper describes the plant and the corresponding models and its control system. Additionally several applications and test are suggested to be performed, including methods for optimization of large scale systems handling uncertain behaviour of process, parameter estimation techniques and distributed model predictive control.

关键词： Laboratory plant Gas networks Large-scale systems Hierarchical decision making Real time optimization distributed model predictive control

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Robust model predictive control Using Linear Matrix Inequalities for the Treatment of Asymmetric Output Constraints

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JOURNAL OF control SCIENCE AND ENGINEERING 2012年第unknown期2012卷 485784.1-485784.7页

作者： Matos Cavalca, Mariana Santos Harrop Galvao, Roberto Kawakami Yoneyama, Takashi Univ Estado Santa Catarina Ctr Ciencias Tecnol Dept Engn Eletr Rua Paulo MalschitzkiZona Ind Norte BR-8921971 Joinville SC Brazil Inst Tecnol Aeronaut Div Engn Elet BR-12228900 Sao Jose Dos Campos SP Brazil

One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the manipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the constraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC) strategies based on linear matrix inequalities (LMIs). However, LMI-based RMPC formulations typically consider only symmetric constraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO systems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A case study using numerical simulation indicates that satisfactory results can be achieved.

关键词： model uncertainty predictive control Constraint linear matrix inequalities distributed model predictive control single input single output systems constraint satisfaction problems nonlinear model predictive control model predictive control

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Implementation Aspects of model predictive control for Embedded Systems

Implementation Aspects of Model Predictive Control for Embed...

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American control Conference

作者： Pablo Zometa Markus Kogel Timm Faulwasser Rolf Findeisen Institute for Automation Engineering OvG University Magdeburg Germany

ISBN: (纸本)9781457710957

We discuss implementation related aspects of model predictive control schemes on embedded platforms. Exemplarily, we focus on fast gradient methods and present results from an implementation on a low-cost microcontroller. We show that input quantization in actuators should be exploited in order to determine a suboptimality level of the online optimization that requires a low number of algorithm iterations and might not significantly degrade the performance of the real system. As a case study we consider a Segway-like robot, modeled by a linear time-invariant system with 8 states and 2 inputs subject to box input constraints. The test system runs with a sampling period of 4 ms and uses a horizons up to 20 steps in a hard real-time system with limited CPU time and memory.

关键词： Embedded systems model predictive control predictive control hard real-time system Time invariant systems gradient methods CPU time Test systems sampling interval actuators distributed model predictive control nonlinear model predictive control Robot

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Robust model predictive control For Automated Trajectory Tracking Of An Unmanned Ground Vehicle

Robust Model Predictive Control For Automated Trajectory Tra...

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American control Conference

作者： Mitra Bahadorian Borislav Savkovic Ray Eaton Tim Hesketh The School of Electrical Engineering and Telecommunications The University of New South Wales The School of Mathematics and Statistics The University of New South Wales

ISBN: (纸本)9781457710957

This work presents a Robust model predictive control (RMPC) approach for trajectory tracking of an Unmanned Ground Vehicle (UGV). In addition to robustness against unknown but bounded disturbances, the controller presented here is also able to deal with constraints on inputs and states due to its formulation as an RMPC. The proposed approach represents an extension of previous model predictive control (MPC) laws based on the concept of constraint restriction (i.e. optimal trajectories via MPC are computed subject to stringent constraints assuming no uncertainty, and a linear local controller ensures that the actual system robustly follows the optimized MPC trajectory). The presented controller carries a low computational complexity overhead, making it attractive for real-time applications. Applying the proposed control approach to the UGV trajectory tracking problem, simulation results demonstrate robust UGV automated trajectory tracking.

关键词： trajectory tracking Unmanned Ground Vehicle distributed model predictive control nonlinear model predictive control Maximum Allowable Concentration model predictive control predictive control real-time application program Constraint

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