Finally, in Section 5.6 we provide an overview of the general area of (hierarchical) intelligent and autonomous control where we offer some ideas on how to define the field of intelligent control and how some of the most general intelligent controllers operate. We use an "intelligent vehicle highway system"problem to illustrate the use of the intelligent autonomous controller functional architecture This chapter is meant to provide a view of, and motivation for, the main areas in the field of intelligent control. The reader interested only in fuzzy control can certainly ignore this chapter; we do not, however, advise this as the relationships to other fields often suggest ideas on how to expand the basic fuzzy control methods and may provide key deas on how to solve a control problem for a particular application 5.2 Fuzzy Versus Conventional Control What are the advantages and disadvantages of fuzzy control as compared to conventional control? What are the perspectives of conventional control engineers on fuzzy control? In this section we will attempt to give answers to these questions by asking, and at least partially answering, a series of questions that we have accumulated over the years from a variety of engineers in industry and universities concerned about whether to use fuzzy or conventional control. We break the questions into three categories and use the questions to summarize several points made in earlier chapters 5.2.1 Modeling Issues and Design Methodology First, we will discuss several issues related to modeling and the overall fuzzy controller design methodology Is the fuzzy controller design methodology viable? Success in a variety of applications (.g, the flexible-link rob application studied in this book)has proven fuzzy control to be a viable methodology and therefore worthy of consideration 2. Do engineers like the methodology? Some do, and some do not. Engineers who have found success with it tend to like it. Often, we find that if engineers invest the time into learning it, they find it to be a tool with which they are comfortable working(they feel like it is"one more tool in their toolbox"). This may be because fuzzy systems are interpolators and engineers are used to thinking about using interpolation as a solution to a wide variety of problems 3. Will the methodology always work? No. The reason we can be so definite in this answer is that it is not the methodology that ultimately leads to success; it is the clever ideas that the control engineer uses to achieve high-performance control. Fuzzy control is a vehicle, and the engineer is the driver. Some find that the vehicle is comfortable and that they can coax it into performing all kinds of functions for them. Others are not so comfortable with 4. Does the design methodology always shorten the"lead time"to design and implementation? In talking with many people in industry, we have found that most often it does(and this is very important, especially in today's competitive climate), but we have also heard of instances where people factor in the cost of having their engineers learn the method and then found the membership functions very hard to tune. In these cases the clear answer from the engineers was that it did not make things easier. We have heard from some that fuzzy logic implements, in a similar way, the standard logic and interpolation methods they already use. Sometimes such engineers find that the fuzzy control jargon clouds the issues that are central to the control problem. Others like that it helps to formalize what they have been doing and helps to suggest ideas for other approaches 5. Is a model used in the fuzzy control design methodology? It is possible that a mathematical model is not used However, often it is used in simulation to redesign a fuzzy controller. Others argue that a model is always used: even if itFinally, in Section 5.6 we provide an overview of the general area of (hierarchical) intelligent and autonomous control where we offer some ideas on how to define the field of intelligent control and how some of the most general intelligent controllers operate. We use an "intelligent vehicle highway system" problem to illustrate the use of the intelligent autonomous controller functional architecture. This chapter is meant to provide a view of, and motivation for, the main areas in the field of intelligent control. The reader interested only in fuzzy control can certainly ignore this chapter; we do not, however, advise this as the relationships to other fields often suggest ideas on how to expand the basic fuzzy control methods and may provide key ideas on how to solve a control problem for a particular application. 5.2 Fuzzy Versus Conventional Control What are the advantages and disadvantages of fuzzy control as compared to conventional control? What are the perspectives of conventional control engineers on fuzzy control? In this section we will attempt to give answers to these questions by asking, and at least partially answering, a series of questions that we have accumulated over the years from a variety of engineers in industry and universities concerned about whether to use fuzzy or conventional control. We break the questions into three categories and use the questions to summarize several points made in earlier chapters. 5.2.1 Modeling Issues and Design Methodology First, we will discuss several issues related to modeling and the overall fuzzy controller design methodology. 1. Is the fuzzy controller design methodology viable? Success in a variety of applications (e.g., the flexible-link robot application studied in this book) has proven fuzzy control to be a viable methodology and therefore worthy of consideration. 2. Do engineers like the methodology? Some do, and some do not. Engineers who have found success with it tend to like it. Often, we find that if engineers invest the time into learning it, they find it to be a tool with which they are comfortable working (they feel like it is "one more tool in their toolbox"). This may be because fuzzy systems are interpolators and engineers are used to thinking about using interpolation as a solution to a wide variety of problems. 3. Will the methodology always work? No. The reason we can be so definite in this answer is that it is not the methodology that ultimately leads to success; it is the clever ideas that the control engineer uses to achieve high-performance control. Fuzzy control is a vehicle, and the engineer is the driver. Some find that the vehicle is comfortable and that they can coax it into performing all kinds of functions for them. Others are not so comfortable with it. 4. Does the design methodology always shorten the "lead time" to design and implementation? In talking with many people in industry, we have found that most often it does (and this is very important, especially in today's competitive climate), but we have also heard of instances where people factor in the cost of having their engineers learn the method and then found the membership functions very hard to tune. In these cases the clear answer from the engineers was that it did not make things easier. We have heard from some that fuzzy logic implements, in a similar way, the standard logic and interpolation methods they already use. Sometimes such engineers find that the fuzzy control jargon clouds the issues that are central to the control problem. Others like that it helps to formalize what they have been doing and helps to suggest ideas for other approaches. 5. Is a model used in the fuzzy control design methodology? It is possible that a mathematical model is not used. However, often it is used in simulation to redesign a fuzzy controller. Others argue that a model is always used: even if it