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Versatility of Particle Swarm Optimization in Identification for Continuous-Time Systems
Toshiharu Sugie,Takashi Wada 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper shows the versatility of the Particle Swarm Optimization, which attracts alot of attention recently in the evolutionary computation are a due to its empirical evidence of its superiority, in the are a of continuous-time system identification. First, a method to identify(possibly nonlinear) continuous-time systems is shown, which uses the Particle Swarm Optimization to minimize the mean square prediction error. Second, its effectiveness is demonstrated through various numerical examples which include time-delay systems, closed loop ones and nonlinear ones.
Mohd Ashraf Ahmad,Shun-ichi Azuma,Toshiharu Sugie 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
This paper performs an initial study on identification of continuous-time Hammerstein models based on Simultaneous Perturbation Stochastic Approximation (SPSA). While the structure information such as the system order is available for the linear subsystems, the structure of nonlinear subsystem is assumed to be completely unknown. For handling it, a piecewise-linear functions are used as a tool to approximate the unknown nonlinear functions. The SPSA based method is then used to estimate the parameters in both the linear and nonlinear parts based on the given input and output data. A numerical example is given to illustrate that the SPSA based algorithm can give an accurate parameter estimation of the Hammerstein models with high probability through detailed simulation.
Dynamic Quantizers for SIMO Control Systems with Unstable Zeros
Yuki Minami,Shun-ichi Azuma,Toshiharu Sugie 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, we present multirate sampling type dynamic quantizers for linear control systems with discrete-valued signal constraints. Although our previous works have derived an optimal dynamic quantizer and verified its effec-tiveness by simulations and experiments, the result can not be applied to SIMO plants with unstable zeros. This paper over comes the draw back by using a multirate sampling technique. We first for mulate an optimal design problem for a class of multirate sampling type dynamic quantizers, and then we give a closed form solution to the problem.
Kim, Tae-Hyoung,Maruta, Ichiro,Sugie, Toshiharu,Chun, Semin,Chae, Minji Hindawi Limited 2017 Mathematical problems in engineering Vol.2017 No.-
<P>This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO) is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.</P>