For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated. Defuzzification strategies, are discussed. Some of the representative applications of the FLC, from laboratory level to industrial process control, are briefly reported. Some unsolved problems are described, and further challenges in this field are discussed. >

Fuzzy logic in control systems: fuzzy logic controller. II

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory. Fuzzy control is based on fuzzy logic. The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. A survey of the FLC is presented; a general methodology for constructing an FLC and assessing its performance is described; and problems that need further research are pointed out

Fuzzy logic in control systems : fuzzy logic controller. Part II

This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

An Introduction to Fuzzy Control

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Fuzzy logic, neural networks, and soft computing

The role of inferencing with uncertainty is becoming more important in rule-based expert systems (ES), since knowledge given by a human expert is often uncertain or imprecise. We have succeeded in designing a VLSI chip which can perform an entire inference process based on fuzzy logic. The design of the VLSI fuzzy inference engine emphasizes simplicity, extensibility, and efficiency (operational speed and layout area). It is fabricated in 2.5 mm CMOS technology. The inference engine consists of three major components; a rule set memory, an inference processor, and a controller. In this implementation, a rule set memory is realized by a read only memory (ROM). The controller consists of two counters. In the inference processor, one data path is laid out for each rule. The number of the inference rule can be increased adding more data paths to the inference processor. All rules are executed in parallel, but each rule is processed serially. The logical structure of fuzzy inference proposed in the current paper maps nicely onto the VLSI structure.A two-phase nonoverlapping clocking scheme is used. Timing tests indicate that the inference engine can operate at approximately 20.8 MHz. This translates to an execution speed of approximately 80,000 Fuzzy Logical Inferences Per Second (FLIPS), and indicates that the inference engine is suitable for a demanding real-time application. The potential applications include decision-making in the area of command and control for intelligent robot systems, process control, missile and aircraft guidance, and other high performance machines.

Expert System on a Chip: An Engine for Real-Time Approximate Reasoning

Analysis and design of a discrete control system that can improve its performance in the course of operation is described in this paper. Such a system is called "discrete learning systems." A new discrete learning algorithm for controlling electro-mechanical systems such as industrial robots is proposed. The algorithm utilizes the error of state variables of the system, which includes positional and velocity error. A condition of algorithmic convergence is obtained. Advantages of a discrete system approach over an analog approach are discussed. Similarity between a learning controller and an state observer is also discussed. In most cases the learning control law is not, uniquely obtained from the condition of algorithmic convergence. We apply system optimization schemes to learning control and identify the learning control law in several ways. We propose better forms for the gain which is defined uniquely. We also show that the gain obtained is a generalized inverse solution to the optimal learning control problem. A discrete learning control scheme based on the proposed algorithm is effectively applied to control an industrial robot.

Analysis and design of an optimal learning control scheme for industrial robots: A discrete system approach

Abstract We present a VLSI implementation of an inference mechanism to cope with uncertainty and to perform approximate reasoning. The design is based on the “max-min operation” of fuzzy set theory for effective and real-time use. This inference mechanism can handle imprecise and uncertain knowledge; therefore, it can obtain human expert knowledge and simulate reasoning processes. An inference mechanism has been realized by using custom CMOS technology which emphasizes simplicity, extensibility, and efficiency. Timing simulation suggests that the inference engine can perform approximately 80,000 fuzzy logical inferences per second. A potential application of such inference engines is real-time decision making in the area of command and control and adaptive command generation of robotic systems.

Masaki Togai

Papers

Expert System on a Chip: An Engine for Real-Time Approximate Reasoning

Analysis and design of an optimal learning control scheme for industrial robots: A discrete system approach

A VLSI Implementation of Fuzzy Inference Engine: Toward an Expert System on a Chip.

A VLSI implementation of a fuzzy-inference engine: toward a expert system on a chip

Expert system on a chip: an engine for real-time approximate reasoning