This paper proposes a novel method of identification of continuous time-delay systems from sampled input-output 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 multiples of the sampling period. Then an identification method combining the genetic algorithm (GA) with the common linear least squares (LS) method or the instrumental variable (IV) method 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 systems where the time-delays in the individual input channels may differ each other. Simulation results show that our method yields consistent estimates even in the presence of high measurement noises.
A ceramic kiln is a two-input/two-output system where temperature and atmosphere (hydrogen or carbon monoxide) inner it are controlled by fuel and a damper. The ceramic kiln has a huge heat capacity and is a nonlinear system because the combustion characteristics in the kiln is always changing due to interference of temperature and atmosphere. Experts, therefore, have been controlling it based on their experimental knowledge. In this paper, we try to control the ceramic kiln by fuzzy control. First, we make a kiln model based on the combustion theory. Next, we choose membership functions of the fuzzy controller while simulating the fuzzy control of the ceramic kiln using the kiln model and fuzzy production rules given by experts. Finally, we make experiments on the fuzzy control for a real ceramic kiln in Shigaraki, Shiga Prefecture, and show some experiment results.
In this paper, the adaptive multi-step learning control for continuous-time linear system is studied. Input function uk+1 (t) is calculated from the triplets (uk, ek, ek) and (uk-1, ek-1, ek-1) where ek is tracking error of k-th trial. The weighting coefficients of the triplets are determined adaptively. The convergence of the control law is proved by means of evaluation of the norm of tracking error. Moreover, the learning control is applied to the tracking problem of two-link robot manipulator.
The concept of parametric absolute stability of Lur'e systems is defined. It provides a mathematical framework for solving the joint problem of feasibility and stability of equilibrium states of Lur'e systems with uncertain parameters and sectorial bounded nonlinearities. This problem arises since stability may be disturbed by the change of equilibrium states which is caused by the parameter variation. In this paper, we consider a single-input single-output Lur'e system, and present Popov-type sufficient conditions for parametric absolute stability. Although the condition contains the parameters, it can be tested by computing the value sets or a family of Popov plots.