Performance Evaluation of PID Controller for an Automobile Cruise Control System using Ant Lion Optimizer

This paper considers the design and performance evaluation of PID controller for an automobile cruise control system (ACCS). A linearized model of the cruise control system has been studied as per the dominant characteristics in closed loop system. The design problem is recast into an optimization problem which is solved using Ant Lion Optimization (ALO). The transient performance of proposed ACCS i.e., settling time, rise time, maximum overshot, peak time and steady state error are investigated by step input response and root locus analysis. To show the efficacy of the proposed algorithm over a state space method, classical PID, fuzzy logic, genetic algorithm, a comparison study is presented by using MATLAB/SIMULINK. Furthermore, the robustness of the system is evaluated by using bode analysis, sensitivity, complimentary sensitivity and controller sensitivity. The results indicate that the designed ALO based PID controller for ACCS achieves better performance than other recent methods reported in the literature.This paper considers the design and performance evaluation of PID controller for an automobile cruise control system (ACCS). A linearized model of the cruise control system has been studied as per the dominant characteristics in closed loop system. The design problem is recast into an optimization problem which is solved using Ant Lion Optimization (ALO). The transient performance of proposed ACCS i.e., settling time, rise time, maximum overshot, peak time and steady state error are investigated by step input response and root locus analysis. To show the efficacy of the proposed algorithm over a state space method, classical PID, fuzzy logic, genetic algorithm, a comparison study is presented by using MATLAB/SIMULINK. Furthermore, the robustness of the system is evaluated by using bode analysis, sensitivity, complimentary sensitivity and controller sensitivity. The results indicate that the designed ALO based PID controller for ACCS achieves better performance than other recent methods reported in the literature

Algoritmos baseados em inteligência de enxames aplicados à multilimiarização de imagens

Orientador: Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 20/08/2018Inclui referências: p.117-122Área de concentração: Sistemas EletrônicosResumo: O processamento de imagens é uma área que cresce à medida que as tecnologias de geração e armazenamento de informações digitais evoluem. Uma das etapas iniciais do processamento de imagem é a segmentação, onde a multilimiarização é uma das técnicas de segmentação mais simples. Um focorelevante de pesquisa nesta área é o projeto de abordagens visando a separação de diferentes objetos na imagem em grupos, por meio de limiares, para facilitar assim a interpretação da informação contida na imagem. Uma imagem perde informação, ou entropia, quando é limiarizada. A equação de limiarização multiníveis de Kapur calcula, a partir dos limiares escolhidos, qual a quantidade de informação que uma imagem apresentará após a limiarização. Assim, pela maximização da equação de multimiliarização de Kapur, é possível determinar os limiares que retornam uma imagem com valor maior de entropia. Quanto maior a quantidade de limiares, maior a dificuldade para encontrar a melhor solução, devido ao aumento significativo da quantidade de possíveis soluções. O objetivo desta dissertação é de apresentar um estudo comparativodecinco algoritmos de otimização (meta-heurísticas de otimização)da inteligência de enxame, incluindo Otimização por Enxame de Partículas (PSO), Otimização por Enxame de Partículas Darwiniano (DPSO), Otimização por Enxame de Partículas Darwiniano de Ordem Fracionária (FO-DPSO), Otimizador baseado no comportamento dos Lobos-cinza (GWO) e Otimizador inspirado no comportamento da Formiga-leão (ALO), de forma a avaliarqual deles obtém a melhor solução e convergência em termos da função objetivo relacionada a entropia da imagem. Uma contribuição desta dissertação é a aplicação de diferentes meta-heurísticas de otimização ao problema de multilimiarização de imagens, assim como o estudo do impacto das suas variáveis de controle (hiperparâmetros) para o problema em questão.Nesta dissertação são apresentados resultados paraquatro imagens diferentes, sendo duas imagens registradas por satélite (Rio Hunza e Yellowstone) e outras duas imagens teste (benchmark) obtidas do Centro de Engenharia Elétrica e Ciência da Computação do MIT (Massachussetts Institute of Technology). Os resultados são comparados considerando a média e o desvio padrão da entropia de cada imagem resultante. Com base nos resultados obtidos conclui-se que o algoritmo mais indicado para o problema de multilimiarização de imagens dos avaliados é o GWO, pelo seu desempenho superior em relação aos outros algoritmos e pelas entropias das imagens resultantes serem satisfatórias. Palavras-chave: Segmentação de imagens. Multilimiarização. Inteligência de enxames. Otimização por enxame de partículas. Otimizador dos lobos-cinza. Otimizador formiga-leão.Abstract: Image processing is a field that grows as digital information storage and generation technologies evolution. One of the initial stages of image processing is segmentation procedure, where the multi level thresholding is one of the simplest segmentation approaches. A relevant research objective in this field is the design of approaches aimed at separating different objects in the image into groups, through thresholds, to facilitate the interpretation of the information contained in the image. An image loses information, or entropy, when it is thresholded. The Kapur multilevel thresholding equation calculates, from the chosen thresholds, how much information an image will present after the thresholding. Thus, by the maximization of the Kapur multilevel limiarization equation, it is possible to determine the thresholds that return an image with a larger value of entropy. The higher the amount of thresholds, the greater the difficulty in finding the best solution, due to the significant increase in the quantity of possible solutions. The objective of this dissertation is to present a comparative study between fiveoptimization metaheuristics of the swarm intelligence field, including Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), Fractional Order Darwinian Particle Swarm Optimization (FO-DPSO), Grey Wolf Optimizer (GWO) and the Ant lion behavioral optimizer (ALO), in order to identify which one gets the best solution and convergence in terms of the objective function and the entropy of the image. A contribution of this dissertation is the application of different optimization metaheuristics to the problem of multilimizing of images, as well as the study of the impact of its control variables (hyperparameters) on the problem in question. Experiments are conducted with four images, two images being recorded by satellite (Hunza River and Yellowstone) and two other test(benchmark) images obtained from MIT's (Massachussetts Institute of Technology) Electrical Engineering and Computer Science Center. The results are compared considering the mean and standard deviation values of each resulting image entropy.Based on the results obtained it is concluded that the most suitable algorithm for the problem of multilevel thresholding of images is the GWO, for its superior performance in relation to the other tested algorithms and satisfactory entropies of the resulting images. Key-words: Image segmentation. Multilevel thresholding. Kapur's entropy. Swarm intelligence. Particle swarm optimization. Grey wolf optimizer. Ant lion optimizer

Laboratory directed research and development program, FY 1996

The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) Laboratory Directed Research and Development Program FY 1996 report is compiled from annual reports submitted by principal investigators following the close of the fiscal year. This report describes the projects supported and summarizes their accomplishments. It constitutes a part of the Laboratory Directed Research and Development (LDRD) program planning and documentation process that includes an annual planning cycle, projection selection, implementation, and review. The Berkeley Lab LDRD program is a critical tool for directing the Laboratory`s forefront scientific research capabilities toward vital, excellent, and emerging scientific challenges. The program provides the resources for Berkeley Lab scientists to make rapid and significant contributions to critical national science and technology problems. The LDRD program also advances the Laboratory`s core competencies, foundations, and scientific capability, and permits exploration of exciting new opportunities. Areas eligible for support include: (1) Work in forefront areas of science and technology that enrich Laboratory research and development capability; (2) Advanced study of new hypotheses, new experiments, and innovative approaches to develop new concepts or knowledge; (3) Experiments directed toward proof of principle for initial hypothesis testing or verification; and (4) Conception and preliminary technical analysis to explore possible instrumentation, experimental facilities, or new devices