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An LMI Approach for Output Feedback Robust Predictive Control using Orthonormal Basis Functions

An LMI Approach for Output Feedback Robust Predictive Contro...

引用

Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Humberto X.Araújo Gustavo H.C.Oliveira Department of Electrical Engineering Federal University of Bahia (UFBA) Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná(PUCPR)Rua Imaculada Concei?o

This paper is focused on the problem of uncertain process control by using RMPC（Robust Model Predictive Control）.A relevant class of RMPC algorithms is the one characterized by the use of the LMI *** field started in the middle of nineties and since then several works applying LMIs in the context of RMPC have been *** of them assume a polytopic representation of the process uncertainties and require full-state *** the other hand,several works describing the theory and applicability of OBF（Orthonormal Basis Functions） in identification and control fields can be found in the ***,the present paper proposes the use of OBF for uncertain process modelling and for RMPC algorithm *** is shown that,due to the proposed approach,one obtains an output feedback control law,with set-point *** represents an advantage in actual ***,well established state feedback RMPC strategies can be reviewed under the OBF modelling *** results illustrate the proposed methodology.

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Discrete Differential Evolution with Local Search to Solve the Traveling Salesman Problem: Fundamentals and Case Studies

Discrete Differential Evolution with Local Search to Solve t...

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7th IEEE International Conference on Cybernetic Intelligent systems

作者： Sauer, Joao Guilherme Coelho, Leandro dos Santos Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PPGEPS/PUCPR) Curitiba Parana Brazil

ISBN: (纸本)9781424429141

Traveling Salesman Problem (TSP) is one of the most difficult problems in the Combinatorial Optimization area. The goal of TSP is to find one path that can travel between all the nodes (instances) of the graph just once (Hamiltonian tour) in the smallest tour, that is, smallest Euclidian distance. In this context, many other meta-heuristics techniques based on evolutionary algorithms have been propose in literature to solve TSP problems. Differential Evolution (DE) is a relatively new simple evolutionary algorithm, which is an effective adaptive approach to global optimization over continuous search spaces. The design principles of DE are simplicity and efficiency. The most distinct feature of DE is that it mutates vectors by adding weighted. random vector differentials to them. Since its invention. DE has been applied with high success on many numerical optimization problems outperforming other more popular meta-heuristics such as the genetic algorithms. Recently. some researchers extended with success the application of DE to complex combinatorial optimization problems with discrete decision variables such as the traveling salesman problem, the machine layout problem, the now-shop scheduling problem. In this paper, the following discrete DE approaches for the TSP are proposed and evaluated: i) DE approach without local search, ii) DE with local search based on Lin-Kernighan-Heulsgaun (LKH) method, and iii) DE with local search based on Variable Neighborhood Search (VNS) and together with LKH method. Numerical study is carried out using the TSPLIB of test TSP problems. In this context, the computational results are compared with the other results in the recent TSP literature. The obtained results show that LKH method is the best method to reach optimal results for TSPLIB benchmarks, but for largest problems, the ED+VNS improve the quality of obtained results.

关键词： Optimization Traveling salesman problem Evolutionary Algorithm Differential Evolution Variable Neighbor Search Local Search Lin-Kernighan-Heulsgaun

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Quantum Gaussian Particle Swarm Optimization approach for PID controller design in AVR system

Quantum Gaussian Particle Swarm Optimization approach for PI...

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2008 IEEE International Conference on systems, Man and Cybernetics, SMC 2008

作者： Coelho, Leandro Dos Santos Herrera, Bruno Á Vila De Meirelles Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Parana Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil

During the history of science of computational intelligence, many evolutionary algorithms approaches were proposed having more or less success in solving various optimization problems. In this context, the Particle Swarm Optimization (PSO) is a bio-inspired optimization mechanism based on the metaphor of social behaviour of birds flocking and fish schooling in search for food. Inspired by the classical PSO method and quantum mechanics theories, this work presents a Quantum-behaved PSO (QPSO) approach using Gaussian probability distribution function (G-QPSO). Numerical simulations based on optimized proportional-integral-derivative (PID) control of an automatic regulator voltage system for nominal system parameters and step reference voltage input demonstrate the effectiveness and efficiency of G-QPSO approach. Simulation results of G-QPSO to determine the PID parameters are compared with the classical PSO and QPSO. © 2008 IEEE.

关键词： Quantum theory

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Discrete Differential Evolution with Local Search to Solve the Traveling Salesman Problem: Fundamentals and Case Studies

Discrete Differential Evolution with Local Search to Solve t...

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IEEE International Conference on Cybernetic Intelligent systems

作者： Joao Guilherme Sauer Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PPGEPS/PUCPR)

ISBN: (纸本)9781424429141

Traveling Salesman Problem (TSP) is one of the most difficult problems in the Combinatorial Optimization area. The goal of TSP is to find one path that can travel between all the nodes (instances) of the graph just once (Hamiltonian tour) in the smallest tour, that is, smallest Euclidian distance. In this context, many other meta-heuristics techniques based on evolutionary algorithms have been propose in literature to solve TSP problems. Differential Evolution (DE) is a relatively new simple evolutionary algorithm, which is an effective adaptive approach to global optimization over continuous search spaces. The design principles of DE are simplicity and efficiency. The most distinct feature of DE is that it mutates vectors by adding weighted, random vector differentials to them. Since its invention, DE has been applied with high success on many numerical optimization problems outperforming other more popular meta-heuristics such as the genetic algorithms. Recently, some researchers extended with success the application of DE to complex combinatorial optimization problems with discrete decision variables such as the traveling salesman problem, the machine layout problem, the flow-shop scheduling problem. In this paper, the following discrete DE approaches for the TSP are proposed and evaluated: i) DE approach without local search, ii) DE with local search based on Lin-Kernighan-Heulsgaun (LKH) method, and iii) DE with local search based on Variable Neighborhood Search (VNS) and together with LKH method. Numerical study is carried out using the TSPLIB of test TSP problems. In this context, the computational results are compared with the other results in the recent TSP literature. The obtained results show that LKH method is the best method to reach optimal results for TSPLIB benchmarks, but for largest problems, the ED+VNS improve the quality of obtained results.

关键词： Optimization Traveling salesman problem Evolutionary Algorithm Differential Evolution Variable Neighbor Search Local Search Lin-Kernighan-Heulsgaun travelling salesman problems evolutionary algorithm MT design theory continuous search

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Quantum Gaussian particle swarm optimization approach for PID controller design in AVR system

Quantum Gaussian particle swarm optimization approach for PI...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Bruno Avila de Meirelles Herrera Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

During the history of science of computational intelligence, many evolutionary algorithms approaches were proposed having more or less success in solving various optimization problems. In this context, the Particle Swarm Optimization (PSO) is a bio-inspired optimization mechanism based on the metaphor of social behaviour of birds flocking and fish schooling in search for food. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-behaved PSO (QPSO) approach using Gaussian probability distribution function (G-QPSO). Numerical simulations based on optimized proportional-integral-derivative (PID) control of an automatic regulator voltage system for nominal system parameters and step reference voltage input demonstrate the effectiveness and efficiency of G-QPSO approach. Simulation results of G-QPSO to determine the PID parameters are compared with the classical PSO and QPSO.

关键词： Particle swarm optimization Three-term control Control systems Quantum mechanics History Computational intelligence Evolutionary computation Birds Marine animals Educational institutions

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Fuzzy model and particle swarm optimization for nonlinear identification of a Chua's oscillator

Fuzzy model and particle swarm optimization for nonlinear id...

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2007 IEEE International Conference on Fuzzy systems, FUZZY

作者： Araujo, Ernesto Dos Santos Coelho, Leandro IEEE Cx.P. 515 12.227-010 Sao Jose dos Campos SP Brazil Production and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Parana Imaculada Conceicao 1155 80.215-901 Curitiba PR Brazil

ISBN: (纸本)1424412102

The identification of a nonlinear system with chaotic behavior denoted Chua's oscillator by using fuzzy models intertwined with particle swarm optimization (PSO) method is presented. This hybrid approach is applied to experimental data generated by an inductorless Chua's circuit that consists of an electronic chaotic oscillator. Chua's circuit has been used as a test platform by various scientific and engineering communities related to the study of chaos due its great potencial in technological applications, for instance, telecommunications, cryptography and physics. Fuzzy set theory has been evolved as a powerful modeling tool that can cope with uncertainties and nonlinearities in modeling and identification procedures. The identification of a optimized fuzzy model Takagi-Sugeno (T-S) fuzzy model involves two primary tasks: parameter tuning and structure optimization. The premise part of production rules is optimized here by using the particle swarm optimization method. In turn, least mean squares technique is applied to the consequent part of a T-S fuzzy model. Results indicate PSO method and Least Mean Square technique succeeded in constructing a T-S fuzzy model when dealing with chaotic dynamics obtained through experimental data supplied by inductorless Chua's electronic circuit. © 2007 IEEE.

关键词： Fuzzy set theory

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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2007 IEEE International Conference on systems, Man, and Cybernetics, SMC 2007

作者： Dos Santos Coelho, Leandro Mariani, Viviana Coceo Industrial and Systems Engineering Graduate Program - PPGEPS Automation and Systems Laboratory Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Pontifical Catholic University of Paraná Mechanical Engineering Graduate Program PUCPR/PPGEM Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)1424409918

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an Implicit Filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. © 2007 IEEE.

关键词： Particle swarm optimization (PSO)

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On selecting the MIMO Generalized Orthonormal Basis Functions poles by using Particle Swarm Optimization

On selecting the MIMO Generalized Orthonormal Basis Function...

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European Control Conference (ECC)

作者： Bruno C. Reginato Gustavo H. C. Oliveira Industrial and Systems Engineering Graduate Program (PPGEPS) of the Pontifical Catholic University of Parana (PUCPR)

This paper deals with the problem of finding the pole(s) in Orthonormal Basis Functions (OBF) models. In fact, any stable system can be exactly represented by an infinite orthonormal functions series, assuming that the basis used in such representation is complete. However, in practice a truncated basis functions expansion is used. For certain basis choice and fixed number of functions, there is a pole or set of poles that minimizes the truncation error. The problem is then to find such set. Therefore, the present paper is focused on the problem of finding the basis functions poles when more than one dynamic is involved, for instance, in SISO Generalized Orthonormal Basis Functions (GOBF) models or MIMO Laguerre, Kautz and/or GOBF models. It is proposed and analyzed here the use of Particle Swarm Optimization (PSO) for such problem. Simulation results presented shows the validity of the proposed method.

关键词： Convergence Mathematical model Particle swarm optimization Approximation methods Optimization MIMO Equations

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program-PPGEPS Automation and Systems Laboratory PUCPR/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Graduate Program PUCPR/PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an implicit filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects.

关键词： Chaos Particle swarm optimization Fuel economy Stochastic processes Psychology Social factors Filtering theory System testing Thermal loading Cost function

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Fuzzy Model and Particle Swarm Optimization for Nonlinear Identification of a Chua's Oscillator

Fuzzy Model and Particle Swarm Optimization for Nonlinear Id...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Ernesto Araujo Leandro dos Santos Coelho Instituto Nacional de Pesquisas Espaciais (INPE) Integration and Testing Laboratory Brazil Production and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Pernambuco Brazil

关键词： Particle swarm optimization Oscillators Chaos Circuit testing Nonlinear systems Fuzzy systems Hybrid power systems Power engineering and energy Cryptography Physics

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