Browsing by Author "Hung, MH"

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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 particle swarm optimization in multi-objective problems
(2006)
Ho, SJ
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Ku, WY
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Jou, JW
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Hung, MH
;
Ho, SY
In this paper, we proposes a novel intelligent multi-objective particle swarm optimization (IMOPSO) to solve multi-objective optimization problems. High performance of IMOPSO mainly arises from two parts: one is using generalized Pareto-based scale-independent fitness function (GPSISF) can efficiently given all candidate solutions a score, and then decided candidate solutions level. The other one is replacing the conventional particle move process of PSO with an intelligent move mechanism (IMM) based on orthogonal experimental design to enhance the search ability. IMM can evenly sample and analyze from the best experience of an individual particle and group particles by using a systematic reasoning method, and then efficiently generate a good candidate solution for the next move of the particle. Some benchmark functions are used to evaluate the performance of IMOPSO, and compared with some existing multi-objective evolution algorithms. According to experimental results and analysis, they show that IMOPSO performs well.