Browsing by Author "Shu, LS"

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Designing structure-specified mixed H-2/H-infinity optimal controllers using an intelligent genetic algorithm IGA
(2005-11-01)
Ho, SJ
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Ho, SY
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Hung, MH
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Shu, LS
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Huang, HL
This brief proposes an efficient method for designing accurate structure-specified mixed H-2/H-infinity optimal controllers for systems with uncertainties and disturbance using an intelligent genetic algorithm (IGA). The newly-developed IGA with intelligent crossover based on orthogonal experimental design (OED) is efficient for solving intractable engineering problems with lots of design parameters. The IGA-based method without using prior domain knowledge can efficiently solve design problems of multi-input-multi-output (MIMO) optimal control systems, which is very suitable for practical engineering designs. High performance and validity of the proposed method are evaluated by two test problems, a MIMO distillation column model and a MIMO super maneuverable F18/HARV fighter aircraft system. It is shown empirically that the IGA-based method has good tracking performance, robust stability and disturbance attenuation for both controllers, compared with the existing methods.
Intelligent evolutionary algorithms for large parameter optimization problems
(2004-12-01)
Ho, SY
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Shu, LS
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Chen, JH
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生物資訊及系統生物研究所
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Institude of Bioinformatics and Systems Biology
This paper proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents into N pairs of gene segments, economically identifying the potentially better one of two gene segments of each pair, and systematically obtaining a potentially good approximation to the best one of all combinations using at most 2N fitness evaluations. IMOEA utilizes a novel generalized Pareto-based scale-independent fitness function for efficiently finding a set of Pareto-optimal solutions to a multiobjective optimization problem. The advantages of IEA and IMOEA are their simplicity, efficiency, and flexibility. It is shown empirically that IEA and IMOEA have high performance in solving benchmark functions comprising many parameters, as compared with some existing EAs.
Optimizing fuzzy neural networks, for tuning PID controllers using an orthogonal simulated annealing, algorithm OSA
(2006-06-01)
Ho, SJ
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Shu, LS
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Ho, SY
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生物科技學系
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生物資訊及系統生物研究所
In this paper, we formulate an optimization problem of establishing a fuzzy neural network model (FNNM) for efficiently tuning proportional-integral-derivative (PID) controllers of various test plants with under-damped responses using a large number P of training plants such that the mean tracking error J of the obtained P control systems is minimized. The FNNM consists of four fuzzy neural networks (FNNs) where each FNN models one of controller parameters (K, T-i, T-d, and b) of PID controllers. An existing indirect, two-stage approach used a dominant pole assignment method with P = 198 to find the corresponding PID controllers. Consequently, an adaptive neuro-fuzzy inference system (ANFIS) is used to independently train the four individual FNNs using input the selected 176 of the 198 PID controllers that 22 controllers with parameters having large variation are abandoned. The innovation of the proposed approach is to directly and simultaneously optimize the four FNNs by using a novel orthogonal simulated annealing algorithm (OSA). High performance of the OSA-based approach arises from that OSA can effectively optimize lots of parameters of the FNNM to minimize J. It is shown that the OSA-based FNNM with P = 176 can improve the ANFIS-based FNNM in averagely decreasing 13.08% error J and 88.07% tracking error of the 22 test plants by refining the solution of the ANFIS-based method. Furthermore, the OSA-based FNNMs using P = 198 and 396 from an extensive timing domain have similar good performance with that using P = 176 in terms of J.
OSA: Orthogonal simulated annealing algorithm and its application to designing mixed H-2/H-infinity optimal controllers
(2004-09-01)
Ho, SJ
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Ho, SY
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Shu, LS
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生物科技學系
;
生物資訊及系統生物研究所
This paper proposes a novel orthogonal simulated annealing (OSA) algorithm for solving intractable large-scale engineering problems and its application to designing mixed H-2/H-infinity optimal structure-specified controllers with robust stability and disturbance attenuation. High performance of OSA arises mainly from an intelligent generation mechanism (IGM), which applies orthogonal experimental design to speed up the search. IGM can efficiently generate a good candidate solution for next move of OSA by using a systematic reasoning method. It is difficult for existing H-infinity- and genetic algorithm (GA)-based methods to economically obtain an accurate solution to the design problem of multiple-input, multiple-output (MIMO) optimal control systems. The high performance and validity of OSA are demonstrated by parametric optimization functions and a MIMO super maneuverable F18/HARV fighter aircraft system with a proportional-integral-derivative (PID)-type controller. It is shown empirically that OSA performs well for parametric optimization functions and the performance of the OSA-based method without prior domain knowledge is superior to those of existing H-infinity- and GA-based methods for designing MIMO optimal controllers.