Discrete Modeling of Uncertain Continuous Systems having an Interval Structure using Genetic Algorithms

C.-C. Hsu, S.-C. Chang, and H.-Y. Kuo (Taiwan)


Discrete modeling, genetic algorithms, uncertain systems, interval plant, discretization, sampled-data systems, parallel computation.


In this paper, an evolutionary approach is proposed to obtain the discrete-time transfer function for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete-time model is first formulated as multiple mono objective optimization problems for coefficients in the discrete model, and subsequently minimized and maximized via a proposed genetic algorithm to obtain the lower and upper bounds of the coefficient functions. The problem of non-linearly coupled coefficients with exponential nature occurred in the exact discrete-time transfer function is therefore circumvented while preserving the interval structure in the resulting discrete model by using this approach. Because of the time consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution processes, parallel computation for the proposed evolutionary approach in a MATLAB-based working environment is therefore proposed to accelerate the derivation process.

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