Model reduction of uncertain discrete systems having an interval structure using genetic algorithms
ISTASC'05 Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation
Discrete Modelling of Continuous-Time Systems Having Interval Uncertainties Using Genetic Algorithms
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Digital redesign of uncertain interval systems via a hybrid particle swarm optimiser
International Journal of Innovative Computing and Applications
Robust control of interval plants using genetic algorithms
Control and Intelligent Systems
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Design of an optimal controller minimizing the integral of squared error (ISE) of the closed-loop system for an interval plant via evolutionary approaches is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem from the signal energy point of view, and subsequently solved by two interactive genetic algorithms. To ensure robust stability of the closed-loop system, root locations of the Kharitonov polynomials associated with the characteristic polynomial are used to establish a constraint handling mechanism for incorporation into the fitness function to effectively evaluate chromosomes in the current population. To accelerate the derivation process to obtain the optimal controller, alternative approaches based on the two-phase evolutionary scheme are also proposed, in which the worst-case ISE is suitably estimated via information provided by the Kharitonov plants. Thus, the derived controller not only stabilizes the interval plant, but also minimizes the ISE criterion of the closed-loop system. Constraints on higher order plants and controller order commonly encountered by conventional design methods are therefore removed by using the proposed approach.