Expert System, Fuzzy Logic, and Neural Network Applications in Power Electronics and Motion Control

Expert System, Fuzzy Logic, and Neural Network Applications in Power Electronics and Motion Control BIMAL K. BOSE, FELLOW, IEEE Invited Paper Artificial intelligence (AI) tools, such as expert system, fuzzy are difficult to solve in traditional way? In early age, it logic, and neural network are expected to usher a new era in power was perceived that human brain takes decision on the basis electronics and motion control in the coming decades. Although of “yes-no” or “true-false” reasoning. In 1854, George these technologies have advanced significantly in recent years and have found wide applications, they have hardly touched the power Boole first published his article “Investigations on the electronics and mackine drives area. The paper describes these laws of thought,” and Boolean algebra and set theory Ai tools and their application in the area of power electronics were born as a result. Gradually, the advent of electronic and motion control. The body of the paper is subdivided into logic and solid state IC’s ushered the modem era of Von three sections which describe, respectively, the principles and Neumann type digital computation. Digital computers were applications of expert system, fuzzy logic, and neural network. The theoretical portion of each topic is of direct relevance to defined as “intelligent” machines because of their capability the application of power electronics. The example applications to process human thought-like yes (Itno (0) logic. Of in the paper are taken from the published literature. Hopefully, course, using the same binary logic, computers can solve the readers will be able to formulate new applications from these complex scientific, engineering, and other data processing examples. problems. Since the 1960’s and in the early 1970’s, it was felt that computers have severe limitations being able to I. INTRODUCTION handle only algorithmic-type problems. An entirely new Artificial Intelligence is machine emulation of the human way of structuring software that closely matches the human thinking processes. The term began to be systematically thinking process, called “Expert System” was bom. The used since the Dartmouth College conference in 1956 new branch of software engineering is called “Knowledge when “artificial intelligence” was defined as “computer Engineering.” This new breed of “Knowledge Engineers” processes that attempt to emulate the human thought pro- was responsible for the acquisition of knowledge from the cesses that are associated with activities that require the human experts in a particular domain and translating it into use of intelligence.” Human brain is the most complex software. In the 1980’s, expert system applications pro- machine on earth. For a long time, the neuro-biologists lifereated in industrial process control, medicine, geology, have been taking the bottom-up approach to understand agriculture, information management, military science, and the brain structure and its functioning, and the behavioral space technology, just to name a few. scientists, such as psychologists and psychiatrists, the top- Since the mid 1960’s, a new theory called “Fuzzy Logic” down approach to understand the human thinking process. or fuzzy set theory was propounded which gradually helped However, our knowledge about the brain is so inadequate at to supplement the expert system as an AI tool. L. A. present that it is expected to take another 50 to 100 years Zadeh [ 161, the originator of this theory, argued that most to understand the human brain and its thinking process. of human thinking is fuzzy or imprecise in nature, and Since human brain is the ultimate intelligent machine, the therefore, Boolean logic (which is represented by crisp question is: Is it possible to generate such intelligence, “0’ and “I”) cannot adequately emulate the thinking process. or at least a part of it, artificially with the help of a However, the general methodology of reasoning remaining computer so that it can solve our complex problems which the same, it was defined as “fuzzy expert system.” In recent Manuscript received November 29, 1993. years, fuzzy logic has emerged as an important AI tool The author is with the Department of Electrical Engineering, The University of Tennessee, Knoxville, TN 37996 USA. to characterize and control a system whose model is not IEEE Log Number 9402594. known, or ill-defined. It has been widely applied in process 0018-9219/94$04.00 0 1994 IEEE PROCEEDINGS OF THE IEEE. VOL. 82, NO. 8, AUGUST 1994 I303 control, estimation, identification, diagnostics, stock market the human expertise in a certain domain. Consider a power prediction, agriculture, military science, etc. electronics engineer or technician who has a special or While the traditional digital computer is very efficient in domain expertise in the fault diagnosis of a power electronic solving expert system problems and somewhat less efficient system. He has learned or acquired this knowledge by in solving fuzzy logic problems, its inability to solve education and experience over a prolonged period of time. pattem recognition and image processing type problems The question is: Is it possible to embed this knowledge in a was seriously felt since the beginnning of the 1990’s. In computer program so that it can replace the human expert? fact, expert system techniques which held so much promise The answer is “yes,” but we need to recognize that human in the 1980’s, could not fulfill the expected computational thinking is so complex that no computer program, however needs. Therefore, people’s attention was recently focused sophisticated, can ever replace human thinking. The expert on a new branch of AI, called “artificial neural network” system, unlike conventional algorithmic programs which (ANN) or “neural network.” Fundamentally, the human can be described by flowcharts, or finite-state machine brain is constituted of billions of nerve cells, called neurons, programs, are specially structured to resemble the human and these neurons are interconnected to constitute the bio- thinking process. Figure 1 shows the basic elements of the logical neural network. Our thinking process is generated expert system. The core of the expert system is the repre- by the action of this neural network. The ANN tends to sentation of knowledge transferred from the human domain simulate the neural network by electronic computational expert. The domain expert, say the power electronics engi- circuits. The ANN technology is the most generic for neer, may or may not have the requisite software expertise. emulation of human thinking. It has been applied to process Knowledge engineering is a branch of computer science that control, diagnostics, identification, character recognition, deals with the techniques of knowledge representation by robot vision, flight scheduling, financial prediction, etc. The computer software. The knowledge engineer acquires the history of ANN technology is not new. It was gradually knowledge from the domain expert and translates it into evolving since the 1950’s, but the glamor of modem expert system software. The knowledge, as shown, can be digital computer and expert system techniques practically classified into two types: the expert knowledge embedded camouflaged the neural network evolution in the 1960’s and in the knowledge base, and the data, facts, and statements 1970’s. Since the beginning of the 1990’s, neural network that are normally embedded in database for supporting the as AI tool has captivated the attention of practically the expert knowledge. The knowledge base basically consists whole scientific community. This new form of machine of a cluster of production rules, as shown in Fig. 2, where intelligence has suddenly been elevated to transcendental each rule is given by an IF . THEN . , statement. Often, heights. Often, it is held as the greatest technological an expert system is defined as knowledge-based or rule- advance since the invention of the transistor. It is predicted based system. A rule has the premise (or antecedent or to touch almost every scientific and engineering application condition) part in the IF statement and the consequent by the early 21st century. Of course, we need to wait and (or conclusion or action) part in the THEN statement. see to what extent this is true. Each rule is supported by parameters. The parameters This paper is concemed with the application of expert can have numerical, logical, or textual values. In the system, fuzzy logic, and neural network techniques in example rule of Fig. 2, dc link voltage, ac line voltage, and power electronics and motion control systems. With these machine speed are the parameters. A rule is “fired” if the tools, a system is said to be “intelligent,” “learning,” or premise is true, and then the action guided by the THEN have “self-organizing” capability. Traditionally, the design statement is executed. The rules can also be designed to of a control system is dependent on the explicit description handle a limited amount of probability through certainty of its mathematical model and parameters. Often, the model factors and probability-based models, such as Bayesian and the parameters are unknown, or ill-defined. The system, approach. The knowledge content can be easily altered, again, may be complex with nonlinearity and parameter updated as the technology changes, or enhanced on the basis variation problems. An intelligent or self-organizing control of “machine learning.” The inference engine (or control system can identify the model, if

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