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第29届中国控制与决策会议

作者： Linzhou Fu Jianfeng Liu Weirong Liu Zhiwu Huang Qing Peng Hongwei Cao School of Information Science and Engineering Central South University

ISBN: (纸本)9781509046584

The Pantograph-Catenary system（PAC） is the most crucial and vulnerable part in the high-speed *** the high-speed movement of locomotive,the conventional passive control would cause various malfunction of the PAC *** the passive control does not have sufficient ability to avoid the dynamic variation of the contact force between carbon-plate and catenary ***,this paper considers the optimal reference-tracking active control algorithm based on explicit model predictive control（EMPC） to stabilize the contact force of PAC and set it precisely for which there exists experimentally validated ***,the nonlinear model of pantograph with one pair of ON/OFF valves is deduced to describe the correspondence between cylinder pressure and contact ***,the process of an offline computation for the state-space approximate optimal linear affine is ***,the EMPC based on the linear affine is ***,the simulation and experiment result are illustrated to prove the effectiveness of the design.

关键词： Active control explicit model predictive control Pantograph-catenary system Contact force

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PID- explicit predictive Dual Mode control for Double -Water Tank 30

PID- Explicit Predictive Dual Mode Control for Double -Water...

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30th Chinese control and Decision Conference (CCDC)

作者： Xiao, Guannan Liu, Fei Hangnail Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781538612446

A PID-explicit model predictive control (EMPC) based on dual mode strategy is developed in this paper to deal with nonlinear processes. The basic algorithm of EMPC decomposes the controller optimization problem into two steps: offline calculate and online search, which can decrease the online calculation and obtain the explicit function of control law in every state. It is convenient for controller analyzing. The traditional linearization -based nonlinear system control depends on pre-established operating point. By the idea of dual mode control, this paper adopts PID controller for nonlinear area while combining explicit model predictive control for linear area. The proposed algorithm is simulated on a double-water tank, which can decrease calculation time and have a satisfying performance.

关键词： explicit model predictive control PID Dual Mode control

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Complexity reduction in motion cueing algorithm for the ULTIMATE driving simulator

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IFAC-PapersOnLine 2017年第1期50卷 10729-10734页

作者： Munir S. Hovd M. Fang Z. Olaru S. Kemeny A. Engineering Cybernetics Department Norwegian University of Science and Technology Trondheim N-7491 Norway Virtual Reality and Immersive Simulation Center RENAULT Paris France Automatic Control Department Supelec 3 rue Joliot Curie91192 France

The performance of a driving simulator depends on the efficiency of the embedded motion cueing algorithm. An explicit model predictive control was established recently as a pertinent design framework for the motion cueing algorithm. The complexity of the explicit solution increases manifold when the human vestibular model is considered. The present paper focuses on the complexity reduction of explicit solution using low complexity contractive sets for the motion cueing algorithm. The low-complexity explicit controller is formulated for the efficient control of the motion cueing system for the ULTIMATE driving simulator available in Renault. © 2017

关键词： Complexity reduction Contractive set Driving simulator explicit model predictive control low complexity contractive sets Motion cueing algorithm

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A Neural Network Architecture to Learn explicit MPC controllers from Data 21st

A Neural Network Architecture to Learn Explicit MPC Controll...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Maddalena, E. T. Moraes, C. G. da S. Waltrich, G. Jones, C. N. Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Univ Fed Santa Catarina UFSC Florianopolis SC Brazil

We present a methodology to learn explicit model predictive control (eMPC) laws from sample data points with tunable complexity. The learning process is cast in a special Neural Network setting where the coefficients of two linear layers and a parametric quadratic program (pQP) implicit layer are optimized to fit the training data. Thanks to this formulation, powerful tools from the machine learning community can be exploited to speed up the offline computations through high parallelization. The final controller can be deployed via low-complexity eMPC and the resulting closed-loop system can be certified for stability using existing tools available in the literature. A numerical example on the voltage-current regulation of a multicell DC-DC converter is provided, where the storage and on-line computational demands of the initial controller are drastically reduced with negligible performance impact. Copyright (C) 2020 The Authors.

关键词： explicit model predictive control machine learning data-driven control neural networks power electronics

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Multiparametric Linear Programming for control

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第二十七届中国控制会议

作者： Morari M. Jones C.N. Zeilinger M.N. Bari M. Automatic Control Lab ETH ZurichPhysikstrasse 3ETL I28CH-8092 ZurichSwitzerland

ISBN: (纸本)9787811243901

Constrained finite time optimal control problems can be expressed as mathematical programs parameterized by the current state of the system:the so-called multi-parametric *** problems have received a great deal of attention in the control community during the last few years because solving the parametric program is equivalent to synthesizing the optimal state-feedback *** many cases of interest,the resulting synthesized controllers are simple piecewise-affine functions,which enables receding horizon control to be used not only in slowly sampled systems requiring powerful computers but now also in high-speed embedded *** primary limitation of these optimal’explicit solutions’is that the complexity can grow quickly with problem *** this talk I will introduce new methods to compute approximate explicit and online control laws that can trade-off time and space complexity against sub-optimality while providing guarantees of stability and feasibility.

关键词： Parametric linear programming Approximation explicit model predictive control

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Verification of advanced controllers for safety-critical systems

Verification of advanced controllers for safety-critical sys...

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作者： Siaulys, Kestutis University of Cambridge

学位级别：博士

In order to design and deploy a feedback controller in a real application, one must de- termine suitable specifications that the design must meet ("validate"), and then ensure that the chosen specifications have been met ("verify"). In this thesis, we investigate a verification paradigm based on formal methods, such as the Satisfiability Modulo Theories (SMT) and quantifier elimination (Weispfenning's virtual term substitution and quantifier elimination by cylindrical algebraic decomposition) algo- rithms. Any control design requirement (such as satisfactory performance, robustness to uncertainties, stability, etc.) that can be expressed in a first order logic formula can be (in principle) verified by using one of these methods. Consequently, in principle, this allows us to consider problems like general non-convex optimisation, exact computation of structured singular value, and synthesis of non-convex feasible parameter sets. In practice, the generality of algorithms like quantifier elimination by cylindrical algebraic decomposition come with a downside of high running time when applied to more complex systems with more parameters. This, in some cases, limits the complexity of the system that we could consider. Therefore, we focused our attention on control problems such as obtaining an explicit MPC law for a linear time invariant system with a quadratic objective and polytopic constraints, or computation of the structured singular value for a system under parametric (and not norm- bounded) uncertainty. Such problems can be expressed as quantifier elimination problems with a particular quantification structure that allows us to take advantage of a specialised quantifier elimination algorithm - the quantifier elimination by Weispfenning's virtual term substitution procedure that has much lower worst-case running time on these types of prob- lems than quantifier elimination by cylindrical algebraic decomposition algorithm. Despite these constraints, we were able to

关键词： model predictive control (MPC) Formal Methods Quantifier Elimination Cylindrical Algebraic Decomposition Weispfenning's virtual term substitution Satisfiability Modulo Theories (SMT) Robust Flight control Why3 MetiTarski Exclusion Regions Structured Singular Value explicit model predictive control Linear Temporal Logic RECONFIGURE Simulink

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The Active Above-Knee Prosthesis Gait control Based on the EMPC

The Active Above-Knee Prosthesis Gait Control Based on the E...

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Chinese Intelligent Automation Conference (CIAC)

作者： Zhang, Yan Zhang, Yongchang Chen, Lingling Xuan, Bokai Hebei Univ Technol Sch Control Sci & Engn 300130 TJ8 Guangrong Rd Tianjin Peoples R China

ISBN: (纸本)9783662464694;9783662464687

On-line optimization control has been recognized as a superior algorithm for constrained systems. To deal with various disturbances and serious nonlinearity, active above-knee prosthesis (AKP) is treated as constrained switching system in this paper. The kinetic characters of AKP were analyzed when an amputee was walking on level-ground. A piecewise affine model can be built, and the AKP system was divided into 15 subsystems. explicit model predictive control algorithm was applied to switch between subsystems. State-space partitions and explicit control laws are calculated off-line. On-line control signal can be obtained by looking up table. This algorithm can make the prosthesis to track the flexion curve of health limb accurately. Finally, results are shown that this method can tune the control impedance for powered knee prostheses with less energy consumption and high operation speed.

关键词： Active above-knee prosthesis Piecewise affine model explicit model predictive control Prosthesis control

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Voltage control Comparison for Low-Power DC-DC Converters in EVs: PI and explicit MPC 44

Voltage Control Comparison for Low-Power DC-DC Converters in...

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44th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Rossi, Mattia Piegari, Luigi Castelli-Dezza, Francesco Mauri, Marco Carmeli, Maria Stefania Politecn Milan Pzza Leonardo da Vinci 32 I-20132 Milan Italy

ISBN: (纸本)9781509066841

Multilevel converters are used to improve the traction converter of modern electric vehicles (EV). Such topologies can be applied even to low-power converter such as dc-dc auxiliary converters, to improve the EV overall efficiency. The low voltage rating of auxiliary circuits implies the usage of low-cost hardware both for power as well for control platforms. A cost-effective explicit model predictive control (eMPC) is designed for a three-level neutral point clamped step-down dc-dc auxiliary converter and compared to an eMPC for a two-level solution. Both eMPCs are compared to a classical PI-type voltage control.

关键词： electric vehicles dc-dc converter explicit model predictive control multilevel converter

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Nearly-optimal explicit MPC-based Reference Governors with Long Prediction Horizons Generated with Machine Learning 24

Nearly-optimal Explicit MPC-based Reference Governors with L...

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24th International Conference on Process control (PC)

作者： Kis, Karol Klauco, Martin Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Bratislava Slovakia

ISBN: (纸本)9798350347630

The paper shows a procedure for constructing an approximated explicit form of the MPC-based reference governor. MPC-based reference governors are often setup up with long prediction horizons with a significant number of constraints, which forbids using conventional parametric optimisation to obtain the explicit solution. This paper explores the approach of mimicking the behaviour of the MPC-based reference governor with a neural network. The paper shows methods that ensure point-wise satisfaction of process constraints during neural network training. A demonstration using a well-known MIMO process is offered to evaluate control performance.

关键词： explicit model predictive control Neural Networks Reference Governor

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Approximating Nonlinear model predictive controllers using Support Vector Machines

Approximating Nonlinear Model Predictive Controllers using S...

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Australian and New Zealand control Conference (ANZCC)

作者： Dang, Tony Debrouwere, Frederik Hostens, Erik Flanders Make Decis BE-3001 Heverlee Belgium

ISBN: (纸本)9781665416504

Typically, model predictive control (MPC) for highly dynamic systems poses challenges to the computation power needed to optimize the control in real-time. In this paper, we present an explainable methodology to approximate MPCs with low input penalization as a closed form expression, using learning by demonstration. Classical approaches, e.g. using neural networks, result in over-complicated controllers and require huge datasets. In this paper, the prior knowledge on the typical bang-bang behavior of low-input penalized MPC will be exploited to approximate the MPC-law by only sparsely sampling the state space. This is achieved by identifying the switching surface of the sampled MPC-solution using Support Vector Machines (SVMs). The result is a lightweight, interpretable, easy to tune, explicit control law suitable for real-time applications. The methodology is validated in simulation on a benchmark problem from the field of process control (stirred tank reactor), and on a physical setup of a highly dynamic motion control problem (parallel SCARA). The results, both in simulation and experimentally, show that strong approximation can already be obtained by using very light-weight controllers which, for the SCARA, were able to run on a frequency of at least 2kHz on the experimental setup.

关键词： explicit model predictive control bang-bang control machine learning motion control process control

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