Tomohiro Hachino

On-line identification of continuous time-delay systems combining least-squares techniques with a genetic algorithm

This paper proposes a new approach to on-line identi® cation of continuous timedelay systems from sampled input± output data. In order to track the time-varying time-delay and system parameters, the linear recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA) which has a high potential for global optimization. The time-delay is coded into binary bit strings and searched by the GA, while the system parameters are updated by the RLS method. Since only the time delay is searched by the GA, a small population size for the GA is suc cient and hence it is possible to implement the algorithm on line on the digital computers. Furthermore, this method (GALS method) is hybridized with the sequential nonlinear least-squares method which is eŒective in local search, to improve the speed of convergence. Simulation results show that both the GALS and the hybrid methods are quite ec cient. It is also veri® ed that, since the hybrid method is eŒective in both global and local optimizations, it has superior tracking performance over the GALS method especially in the case where the system parameters and time delay vary continuously with time.

A control design of robotics using the genetic algorithm

This paper presents a new and practical method for a control design of a robotic system. In general, actuators in robotic systems are set with gears whose characteristics are elastic. Since a state feedback-type digital controller is usually used for such a robotic system, the design of the feedback gain of the controller is important, because undesirable vibrations or an overshoot in responses occur for high gains. Therefore the desired response, the output of a reference model, is designed first, and the feedback gains are determined so that the response will coincide with the desired response, which is an optimization problem. The gradient method works to some extent, but it takes a long time to get a satisfactory result. Thus we applied the genetic algorithm (GA) to this nonlinear optimization problem, which gave the very first convergence. The gains obtained have many useful applications. The results of a simulation are also given.

Identification of Parameters and Time Delays of Continuous Systems using the Genetic Algorithm

Abstract This report proposes a novel method of identification of continuous time-delay systems from sampled inputoutput data. By the aid of a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of the sampling period. Then an identification method combining the common linear least squares (LS) method or the instrumental variable (IV) method with the genetic algorithm (GA) is proposed. That is, the time-delay is selected by the GA, and the system parameters are estimated by the LS or IV method. Furthermore, the proposed method is extended to the case of multi-input multi-output(MIMO) systems where the time-delays in the individual input channels may differ each other. Simulation results show that our method yields accurate estimates even in the presence of high measurement noises.