Chapter 5 Perspectives on Fuzzy Control
Chapter 5 Perspectives on Fuzzy Control
5.1 Overview Fuzzy control does not exist as an isolated topic devoid of relationships to other fields, and it is important to understand how it relates to these other fields in order to strengthen your understanding of it. We have emphasized that fuzzy control has its foundations in conventional control and that there are many relationships to techniques, i ideas, and methodologies there. Fuzzy control is also an intelligent control technique, and hence there are certain relationships between it and other intelligent control methods
5.1 Overview Fuzzy control does not exist as an isolated topic devoid of relationships to other fields, and it is important to understand how it relates to these other fields in order to strengthen your understanding of it. We have emphasized that fuzzy control has its foundations in conventional control and that there are many relationships to techniques, ideas, and methodologies there. Fuzzy control is also an "intelligent control" technique, and hence there are certain relationships between it and other intelligent control methods
We begin the chapter in Section 5.2 by providing a conventional control engineering perspective on fuzzy control. This is essentially a summary of many of the points that we have made throughout the text, but here we bring them all together. Following this, in Section 5.3 we introduce two popular areas in neural networks, the multilayer perceptron and the radial basis function neural network. We explain that a class of radial basis function neural networks is identical to a class of fuzzy systems Moreover, we explain how techniques covered in this book (e.g., gradient training and adaptive control) can be used for neural networks. In Section 5. 4 we explain genetic algorithms, their relationship to the field of control, and particularly their use with fuzzy systems Next, in Section 5.5 we provide an overview of some of the relationships to knowledge-based systems, particularly expert systems(and hence expert control) and planning systems. 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 generalintelligent controllers operate e
We begin the chapter in Section 5.2 by providing a conventional control engineering perspective on fuzzy control. This is essentially a summary of many of the points that we have made throughout the text, but here we bring them all together. Following this, in Section 5.3 we introduce two popular areas in neural networks, the multilayer perceptron and the radial basis function neural network. We explain that a class of radial basis function neural networks is identical to a class of fuzzy systems. Moreover, we explain how techniques covered in this book (e.g., gradient training and adaptive control) can be used for neural networks. In Section 5.4 we explain genetic algorithms, their relationship to the field of control, and particularly their use with fuzzy systems. Next, in Section 5.5 we provide an overview of some of the relationships to knowledge-based systems, particularly expert systems (and hence expert control) and planning systems. 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
This chapter is meant to provide a view of and motivation for the main areas in the field of intelligent control
This chapter is meant to provide a view of, and motivation for, the main areas in the field of intelligent control
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? 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 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? 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
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 flexiblelink 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. Fuzz 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
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
Is amodel 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 is not written down, some type of model is used "in your head. 6. Since most people claim that no formal model is used in the fuzzy control design methodology, the following questions arise: o() Is it not true that there are few, if any, assumptions to be violated by fuzzy control and that the technique can be indiscriminately applied? Yes, and sometimes it is applied to systems where it is clear that a PID controller or look-up table would be just as effective So, if this is the case, then why not use fuzzy control? Because it is more computationally complex than a PID controller and the PID controller is much more widely understood o(b) Are heuristics all that are available to perform fuzzy controller design? No Any good models that can be used, probably should be
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 is not written down, some type of model is used "in your head." 6. Since most people claim that no formal model is used in the fuzzy control design methodology, the following questions arise: ❖ (a) Is it not true that there are few, if any, assumptions to be violated by fuzzy control and that the technique can be indiscriminately applied? Yes, and sometimes it is applied to systems where it is clear that a PID controller or look-up table would be just as effective. So, if this is the case, then why not use fuzzy control? Because it is more computationally complex than a PID controller and the PID controller is much more widely understood. ❖ (b) Are heuristics all that are available to perform fuzzy controller design? No. Any good models that can be used, probably should be
(c By ignoring a formal model, if it is available, is it not the case that a significant amount of information about how to control the plant is ignored? Yes. If, for example, you have a model of a complex process, we often use simulations to gain an understanding of how best to control the plant and this knowledge can be used to design a fuzzy controller o( d) Can standard control theoretic analysis be used to verify the operation of the resulting control system? Sometimes, if the fuzzy control system satisfies the assumptions needed for the mathematical analysis. This will be discussed in more detail in the next section o(e) Will it be difficult to clearly characterize the limitations of various fuzzy control techniques (i.e, to classify which plants can be controlled best with different fuzzy or conventional controllers)? Yes .o(f Will it be difficult to clearly relate the results of using the fuzzy controller to previous work in conventional control to definitively show that contributions are being made to the field of control? Yes
❖ (c) By ignoring a formal model, if it is available, is it not the case that a significant amount of information about how to control the plant is ignored? Yes. If, for example, you have a model of a complex process, we often use simulations to gain an understanding of how best to control the plant— and this knowledge can be used to design a fuzzy controller. ❖ (d) Can standard control theoretic analysis be used to verify the operation of the resulting control system? Sometimes, if the fuzzy control system satisfies the assumptions needed for the mathematical analysis. This will be discussed in more detail in the next section. ❖ (e) Will it be difficult to clearly characterize the limitations of various fuzzy control techniques (i.e., to classify which plants can be controlled best with different fuzzy or conventional controllers)? Yes. ❖ (f) Will it be difficult to clearly relate the results of using the fuzzy controller to previous work in conventional control to definitively show that contributions are being made to the field of control? Yes
Is there always a formal model available for control design? No, but for most systems there is at least an approximate model available. This information is often valuable and should not be ignored 8. Does the use of fuzzy controllers limit the design methodology as compared to the use of more general expert controllers? Expert controllers use more general knowledge-representation schemes and inference strategies(see more details in Section 8.5.1), so for some plants it may be advantageous to use the expert controller. It is however, not clear at this point what class of plants call for the use of expert control
7. Is there always a formal model available for control design? No, but for most systems there is at least an approximate model available. This information is often valuable and should not be ignored. 8. Does the use of fuzzy controllers limit the design methodology as compared to the use of more general expert controllers? Expert controllers use more general knowledge-representation schemes and inference strategies (see more details in Section 8.5.1), so for some plants it may be advantageous to use the expert controller. It is, however, not clear at this point what class of plants call for the use of expert control