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Distributed Nonlinear Model Predictive Control by Sequential Linearization and Accelerated Gradient Method

IFAC-PapersOnLine 2016年第7期49卷 597-602页

作者： Grancharova, Alexandra Johansen, Tor A. Petrova, Valeria Department of Industrial Automation University of Chemical Technology and Metallurgy Bul. Kl. Ohridski 8 Sofia1756 Bulgaria Center for Autonomous Marine Operations and Systems Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim7491 Norway

A suboptimal approach to distributed NMPC for nonlinear interconnected systems subject to constraints is proposed. The objective is to develop a computationally efficient approach. The suggested method is based on a sequential linearization of the nonlinear system dynamics and finding a suboptimal solution of the resulting quadratic programming problem by using distributed iterations of the dual accelerated gradient method. The benefits of the approach are reduced complexity of the on-line computations, and simple software implementation, which makes it appropriate for embedded distributed convex NMPC. The proposed method is illustrated with simulations on the model of a quadruple-tank system. © 2016

关键词： Distributed computer systems Distributed parameter control systems Gradient methods Large scale systems Linearization Model predictive control Nonlinear systems Predictive control systems quadratic programming Computationally efficient Constraints Distributed control Non linear system dynamics Nonlinear interconnected systems Nonlinear model predictive control Predictive control quadratic programming problems

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A comparison between free motion planning algorithms applied to a quadruped robot leg

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IFAC-PapersOnLine 2015年第19期48卷 19-24页

作者： Potts, Alain Segundo Da Cruz, José Jaime Center of Engineering and Applied Social Sciences Federal University of ABC Santo-Andre-SP Brazil Polytechnic School University of Sao Paulo SP Brazil

In this work a comparison between an optimal power consumption algorithm proposed by the authors and two classical methods for the generation of trajectories is discussed. The proposed algorithm is designed to low level of control, where variables such as terminal states, runtime and physical and electrical parameters of the movement are all provided by higher levels of control. An original complex problem of optimization is transformed into a simple quadratic programming problem subject to linear constraints by discretizing all dynamic system variables. The performance of the algorithm is illustrated by using it in the motion planning of a quadruped robot leg. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Robot programming Electric power utilization Motion planning Multipurpose robots quadratic programming Classical methods Electrical parameter Linear constraints Optimal controls Path control quadratic programming problems Quadruped Robots System variables

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Model predictive control for constrained uncertain, time-varying systems

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IFAC-PapersOnLine 2015年第11 11期48卷 930-935页

作者： Nguyen, Hoai-Nam Gutman, Per-Olof IFP Energies nouvelles Rond-point de lchangeur de Solaize SolaizeBP369360 France Faculty of Civil and Environmental Engineering Technion -Israel Institute of Technology Haifa32000 Israel

In this paper, a new state feedback model predictive control for polytopic uncertain and/or time-varying linear discrete-time systems with input and state constraints is proposed. The main idea is to incorporate state decomposition principle and model predictive control in order to obtain a large domain of attraction using a short prediction horizon. At each time instant a quadratic programming problem is solved on-line. Proofs of recursive feasibility and asymptotic stability are given. © 2015

关键词： Discrete time control systems Asymptotic stability Digital control systems Model predictive control quadratic programming State feedback Time varying control systems Uncertain systems Constrained controls Decomposition principles Feedback model Input and state constraints Linear discrete time systems Polytopic uncertain Prediction horizon quadratic programming problems

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Interconnection and damping assignment for implicit port-Hamiltonian systems

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IFAC-PapersOnLine 2015年第11 11期48卷 1006-1011页

作者： Castaños, Fernando Gromov, Dmitry Automatic Control Department Cinvestav del IPN México D.F. Mexico Faculty of Applied Mathematics St. Petersburg State University St. Petersburg Russia

Implicit port-Hamiltonian representations of mechanical systems are considered from a control perspective. Energy shaping is used for the purpose of stabilizing a desired equilibrium. When using implicit models, the problem turns out to be a simple quadratic programming problem (as opposed to the partial differential equations that need to be solved when using explicit representations). © 2015

关键词： Hamiltonians Pendulums quadratic programming Explicit representation Hamiltonian dynamics Holonomic constraints Implicit model Interconnection and damping assignment Passivity Port Hamiltonian systems quadratic programming problems

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Embedded ADMM-based QP Solver for MPC with polytopic constraints

Embedded ADMM-based QP Solver for MPC with polytopic constra...

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European Control Conference (ECC)

作者： Dang, Thuy V. Ling, K. V. Maciejowski, J. M. Nanyang Technol Univ Interdisciplinary Grad Sch Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Cambridge Cambridge CB2 1TN England Energy Res Inst NTU ERI N Singapore Singapore

ISBN: (纸本)9783952426937

We propose an algorithm for solving quadratic programming (QP) problems with inequality and equality constraints arising from linear MPC. The proposed algorithm is based on the 'alternating direction method of multipliers' (ADMM), with the introduction of slack variables. In comparison with algorithms available in the literature, our proposed algorithm can handle the so-called sparse MPC formulation with general inequality constraints. Moreover, our proposed algorithm is suitable for implementation on embedded platforms where computational resources are limited. In some cases, our algorithm is division-free when certain fixed matrices are computed offline. This enables our algorithm to be implemented in fixed-point arithmetic on a FPGA. In this paper, we also propose heuristic rules to select the step size of ADMM for a good convergence rate.

关键词： embedded systems field programmable gate arrays matrix algebra predictive control quadratic programming FPGA alternating direction method of multipliers computational resources embedded ADMM-based QP solver embedded platforms fixed-point arithmetic inequality constraints linear MPC polytopic constraints quadratic programming problems sparse MPC formulation Algorithm design and analysis Convergence Eigenvalues and eigenfunctions Linear matrix inequalities Mathematical model Prediction algorithms Sparse matrices

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A parallel dual-type algorithm for a class of quadratic programming problems and applications

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 5190-5199页

作者： Lin, Shieh-Shing Lin, Ch'i-Hsin Horng, Shih-Cheng St Johns Univ Dept Elect Engn Taipei 25135 Taiwan Kao Yuan Univ Dept Elect Engn Kaohsiung Taiwan Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan

In this paper, we present a parallel dual-type (PDT) algorithm for solving a strictly convex quadratic programming problem with equality and box constraints. The PDT algorithm is suitable for distributed implementation and can be used as a basic optimization module for handling optimization problems of large distributed systems. Besides, combining the proposed algorithm with a successive quadratic programming (SQP) method, we can solve constrained nonlinear programming problems such as power-system state estimation with power-flow balance constraints on no generation and no-load buses. We have demonstrated the computational efficiency of our method, by comparing with the benchmark commercial NCONF and QPROG routines and the state-of-the-art parallel algorithm through the implementation in the sequential version of Sparc workstation and the parallel version of PC network in solving constrained state estimation problems within IEEE 30-bus and IEEE 118-bus systems. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Optimization Parallel processing Dual method quadratic programming problems Power-system state estimation

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A Revised Neural Network for Solving quadratic programming problems

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6th International Symposium on Neural Networks

作者： Sun, Yinjie Henan Normal Univ Xinxiang 453007 Henan Peoples R China

ISBN: (纸本)9783642015120

By selecting an appropriate transformation of the variables in quadratic programming problems with equality constraints, a lower order recurrent neural network for solving higher quadratic programming is presented. The proposed recurrent neural network is globally exponential stability and converges to the optimal solutions of the higher quadratic programming, An op-amp based on the, analogue circuit, realization of the recurrent neural network is described. The recurrent neural network proposed in the paper is simple in structure, and is more stable and more accuracy for solving the higher quadratic programming than some existed conclusion, especially for the case that the number of decision variables is close to the number of the constraints. An illustrative example is discussed to show us how to design the analogue neural network using the steps proposed in this paper.

关键词： quadratic programming problems Neural Network Analogue Circuit

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Improved Twin Support Vector Machine Using Total Margin and Graph Embedding

Improved Twin Support Vector Machine Using Total Margin and ...

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9th International Conference on Natural Computation (ICNC)

作者： Chen, Xiaobo Mao, Qirong Han, Fei Liang, Jun Jiangsu Univ Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Automot Engn Res Inst Engn Zhenjiang 212013 Peoples R China

ISBN: (纸本)9781467347143

Twin support vector machine (TSVM) was proposed recently as a novel binary classifier which aims to seek a pair of nonparallel planes such that each one is closest to the samples of its own class and is at least one distance far from the samples of the other class. In this paper, we improve TSVM and propose a novel graph embedded total margin twin support vector machine (GTM-TSVM). The central idea of GTM-TSVM is the plane of one class is required to be far away from overall samples of the other class. Moreover, the intra-class and inter-class graphs which respectively characterize the proximity relationships between samples of within and between classes are embedded into GTM-TSVM formulation so as to exploit the underlying manifold structure of data. The nonlinear classification with kernels is also studied. The experimental results on several publicly available benchmark data sets confirm the feasibility and effectiveness of the proposed method.

关键词： Twin support vector machine quadratic programming problems Total margin Graph embedding

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An Efficient Algorithm for QCDP and Implementation

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IEEE TRANSACTIONS ON POWER SYSTEMS 2010年第1期25卷 234-242页

This work formulates optimal power flow with continuous and discrete variables problems (OPFCDP) and state estimation with continuous and discrete variables problems (SECDP) as classes of quadratic programming with continuous and discrete variables problems (QCDP). This work also applies an ordinal optimization (OO) theory-based two-stage algorithm to solve the QCDP. This work first constructed a crude but efficient model to select excellent settings from a sample space. A scheme with enhanced accuracy based on sensitivity theory was applied to rank the samples and identify the top samples to form the selected subset. Finally, these discrete samples in the selected subset were solved by the exact model and the top setting with the smallest objective value was the good enough solution. Via numerous tests, this work demonstrates the efficiency of the proposed algorithm and compares with those of other heuristic methods, such as tabu search, genetic algorithm, and the ant colony system, for solving the SECDP and OPFCDP on IEEE 118-bus and 244-bus systems.

关键词： Optimal power flow with continuous and discrete variables problems ordinal optimization quadratic programming problems state estimation with continuous and discrete variables problems

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NOVEL EXPONENTIAL STABILITY CONDITIONS FOR A CLASS OF INTERVAL PROJECTION NEURAL NETWORKS

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INTERNATIONAL JOURNAL OF BIOMATHEMATICS 2009年第3期2卷 287-297页

作者： Liu, Zixin Lue, Shu Zhong, Shouming Univ Elect Sci & Technol China Sch Appl Math Chengdu 610054 Peoples R China Guizhou Coll Finance & Econ Sch Math & Stat Guiyang 550004 Guizhou Peoples R China

In this paper, a class of interval projection neural networks for solving quadratic programming problems are investigated. By using Gronwall inequality and constructing appropriate Lyapunov functionals, several novel conditions are derived to guarantee the exponential stability of the equilibrium point. Compared with previous results, the conclusions obtained here are suitable not only to convex quadratic programming problems but also to degenerate quadratic programming problems, and the conditions are more weaker than the earlier results reported in the literature. In addition, one numerical example is discussed to illustrate the validity of the main results.

关键词： Projection neural networks exponential stability quadratic programming problems

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