Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robust Control: The Parametric Approach
Robust Control: The Parametric Approach
Robust Control: Systems with Uncertain Physical Parameters
Robust Control: Systems with Uncertain Physical Parameters
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Design of optimal disturbance rejection PID controllers usinggenetic algorithms
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: Zeros of sampled systems
Automatica (Journal of IFAC)
Track following control design for ODDs by employing repetitive two-degree-of-freedom control scheme
IEEE Transactions on Consumer Electronics
Digital redesign of uncertain interval systems via a hybrid particle swarm optimiser
International Journal of Innovative Computing and Applications
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In this paper, a genetic algorithm-based approach is proposed to determine a desired sampling-time range which guarantees minimum phase behaviour for the sampled-data system of an interval plant preceded by a zero-order hold (ZOH). Based on a worst-case analysis, the identification problem of the sampling-time range is first formulated as an optimization problem, which is subsequently solved under a GA-based framework incorporating two genetic algorithms. The first genetic algorithm searches both the uncertain plant parameters and sampling time to dynamically reduce the search range for locating the desired sampling-time boundaries based on verification results from the second genetic algorithm. As a result, the desired sampling-time range ensuring minimum phase behaviour of the sampled-data interval system can be evolutionarily obtained. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution cycles, parallel computation for the proposed genetic algorithm is therefore proposed to accelerate the derivation process. Illustrated examples in this paper have demonstrated that the proposed GA-based approach is capable of accurately locating the boundaries of the desired sampling-time range.