教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Author: search.filters.author.Chen-Chien HsuSubject: search.filters.subject.genetic algorithms

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    Discrete Modelling of Continuous-Time Systems Having Interval Uncertainties Using Genetic Algorithms
    (Institute of Electronics, Information and Communication Engineers, 2008-01-01) Chen-Chien Hsu; Tsung-Chi Lu; Heng-Chou Chen
    In this paper, an evolutionary approach is proposed to obtain a discrete-time state-space interval model for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete interval model is first formulated as multiple mono-objective optimization problems for matrix-value functions associated with the discrete system matrices, and subsequently optimized via a proposed genetic algorithm (GA) to obtain the lower and upper bounds of the entries in the system matrices. To show the effectiveness of the proposed approach, roots clustering of the characteristic equation of the obtained discrete interval model is illustrated for comparison with those obtained via existing methods.
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    Multiobjective Evolutionary Approach to the Design of Optimal Controllers for Interval Plants Based on Parallel Computation
    (Institute of Electronics, Information and Communication Engineers, 2006-09-01) Chen-Chien Hsu; Shih-Chi Chang; Chih-Yung Yu
    Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.�