This study introduces the fuzzy Lyapunov function to the fuzzy PID control systems, modified fuzzy systems, with an optimized robust tracking performance. We propose a compound search strategy called conditional linear matrix inequality (CLMI) approach which was composed of the proposed improved random optimal algorithm (IROA) concatenated with the simplex method to solve the linear matrix inequality (LMI) problem. If solutions of a specific system exist, the scheme finds more than one solutions at a time, and these fixed potential solutions and variable PID gains are ready for tracking performance optimization. The effectiveness of the proposed control scheme is demonstrated by the numerical example of a cart-pole system.

In this paper we discuss an adaptive process, which is based on the so-called self-tuning mechanism. We simplify this mechanism and apply it to a threshold system. From view points of information quantity and estimation accuracy we show this mechanism enhances information transmission through the threshold system. In addition we extend our theory so that it could be applied to a truncation coding.

PID control schemes based on the classical control theory, have been widely used for various real control systems. However, in practice, since it is considerably difficult to determine the PID parameters suitably, lots of researches have been reported with respect to tuning schemes of PID parameters. Furthermore, several self-tuning and auto-tuning techniques in the PID control have been reported for systems with unknown or slowly time-varying parameters. On the other hand, so-called a generalized predictive control (GPC) scheme has been reported as a useful self-tuning control technique for unknown and/or time variant delay systems. In this paper, a new self-tuning predictive PID control algorithm based on a GPC criterion is proposed.

Lots of self-tuning control schemes have been proposed for tuning the parameters of control systems. Among them, pole-assignment schemes have been widely used for tuning the parameters of control systems with unknown time delays. They are usually classified into two methods, the implicit and the explicit methods according to how to identify the parameters. The latter has an advantage to design a control scheme by taking account of the stability margin and control performance. However, it involves a considerably computational burden to solve a Diophantine equation. A simple scheme is proposed in this paper, which can construct a multivariable self-tuning pole-assignment control system, while taking account of the stability margin and control performance without solving a Diophantine equation.

This paper presents a new method for the selftuning control of nonminimum phase discrete-time stochastic systems using approximate inverse systems obtained from the leastsquares approximation. Using this approximate inverse system the gain response of the system can be made approximately unit and phase response exactly zero. We show how unstable polezero cancellations can be avoided. This approximate inverse system can be used in the same manner for both minimum and nonminimum phase systems. Moreover, the degrees of the controller polynomials do not depend on the approximate inverse system. We just need an extra FIR filter in the feedforward path.

We present a new method for the self-tuning control (STC) of nonminimum phase continuous-time systems based on the pole-zero placement. The long division method is used to decompose a polynomial into a stable and unstable polynomials. It is also shown that the effect of unstable zeros on the magnitude of the desired output can be cancelled. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

In order to provide a fuzzy system with learning function, numerous studies are being carried out to combine fuzzy systems and neural networks. The self-tuning methods using the descent method have been proposed. The constructive and the destructive methods are more powerful than other methods using neural networks (or descent method). On the other hand the destructive method is superior in the number of rules and inference error and inferior in learning speed to the constructive method. In this paper, we propose a new learning method combining the constructive and the destructive methods. The method is superior in the number of rules, inference error and learning speed to the destructive method. However, it is inferior in learning speed to the constructive method. Therefore, in order to improve learning speed of the proposed method, simplified learning methods are proposed. Some numerical examples are given to show the validity of the proposed methods.

A fiber-optic transmission system for linking radio base stations to the mobile communication center is developed, and its performance is evaluated. The introduction of this system yields two main improvements: optimum zone allocation to increase radio frequency utilization efficiency and the elimination of service quality issues such as dead zones and traffic imbalance. Being optical, the system suffers from the interferometric noise and distortion created by multiple reflections within the fiber. Moreover, because system response is much different from that of optical CATV systems, we clarify the optical parameter selection criteria and hypothetical return loss model for an embedded fiber infrastructure. An optical multiplexing method is also introduced that reduces the quantity of fiber and connectors, as well as splicing and cable installation costs. A new ternary optical multiplexing architecture combined with a cost-effective self-tuning type WDM technique and a high isolation type circulator are proposed for the 1.3µm wavelength region. The performance of low distortion high power common amplifiers is measured with the aim of reducing the size and weight of back-up batteries, and to improve the packaging density of the typical base station.