Publisher's Synopsis
During the past decades, fuzzy logic control (FLC) has been one of the most active and fruitful areas for research in the application of fuzzy set theory. As an intelligent control technology, fuzzy logic control (FLC) provides a systematic method to incorporate human experience and implement nonlinear algorithms, characterized by a series of linguistic statements, into the controller. In general, a fuzzy control algorithm consists of a set of heuristic decision rules and can be regarded as an adaptive and nonmathematical control algorithm based on a linguistic process, in contrast to a conventional feedback control algorithm. The fuzzy control also works as well for complex nonlinear multi-dimensional system, system with parameter variation problem or where the sensor signals are not precise. It is basically nonlinear and adaptive in nature, giving robust performance under parameter variation and load disturbance effect. It has been an active research topic in automation and control theory, since the work of Mamdani proposed in 1974 based on the fuzzy sets theory of Zadeh, to deal with the system control problems which is not easy to be modeled. The literature in fuzzy control has been growing rapidly in recent years, making it difficult to present a comprehensive survey of the wide variety of applications that have been made. Fuzzy logic, which is the logic on which fuzzy control is based, is much closer in spirit to human thinking and natural language than the traditional logical systems. Basically, it provides an effective means of capturing the approximate and the inexact nature of the real world. The fuzzy logic controller is a set of linguistic control rules related by the dual concepts of fuzzy implication and the compositional rule of inference. The FLC provides an algorithm which can convert the linguistic control strategy based on expert knowledge into an automatic control strategy. The concept of FLC is to utilize the qualitative knowledge of a system to design a practical controller. For a process control system, a fuzzy control algorithm embeds the intuition and experience of an operator designer and researcher. The fuzzy control method is suitable for systems with non-specific models, and therefore, it suits well to a process where the model is unknown or ill-defined and particularly to systems with uncertain or complex dynamics. The implementation of such control consists of translating the input variables to a language like: positive big, zero, negative small, etc. and to establish control rules so that the decision process can produce the appropriate outputs. Fuzzy Logic- Controls and Concepts introduces new concepts and theories of Fuzzy Logic Control for the application and development of robotics and intelligent machines. Fuzzy control (FC) using linguistic information possesses several advantages such as robustness, model-free, universal approximation theorem and rules-based algorithm. The inaccuracy and uncertainty are two aspects that may be part of the information. There are two theories used to deal with inaccuracy and uncertainty: classic sets (crisp) theory and probabilities theory, respectively. However, these theories do not always capture the information content provided by humans in natural language. The classic sets theory cannot deal with the fuzzy aspect of information while the probabilities theory is more suited to handle frequency information than those provided by humans. These theories have been used in systems that use human-provided information. These theories are closely linked with each other. When the fuzzy sets theory is used in a logic context, as knowledge-based systems, it is known as fuzzy logic (term used in this chapter). The fuzzy logic is currently one of the most successful technologies for the development of process control systems, due to low implementation cost, easy maintenance and the fact that complex requirements may be implemented in simple controllers. During the past decades, fuzzy logic control (FLC) has been one of the most active and fruitful areas for research in the application of fuzzy set theory. As an intelligent control technology, fuzzy logic control (FLC) provides a systematic method to incorporate human experience and implement nonlinear algorithms, characterized by a series of linguistic statements, into the controller. In general, a fuzzy control algorithm consists of a set of heuristic decision rules and can be regarded as an adaptive and nonmathematical control algorithm based on a linguistic process, in contrast to a conventional feedback control algorithm. The fuzzy control also works as well for complex nonlinear multi-dimensional system, system with parameter variation problem or where the sensor signals are not precise. It is basically nonlinear and adaptive in nature, giving robust performance under parameter variation and load disturbance effect. It has been an active research topic in automation and control theory, since the work of Mamdani proposed in 1974 based on the fuzzy sets theory of Zadeh, to deal with the system control problems which is not easy to be modeled. The literature in fuzzy control has been growing rapidly in recent years, making it difficult to present a comprehensive survey of the wide variety of applications that have been made. Fuzzy logic, which is the logic on which fuzzy control is based, is much closer in spirit to human thinking and natural language than the traditional logical systems. Basically, it provides an effective means of capturing the approximate and the inexact nature of the real world. The fuzzy logic controller is a set of linguistic control rules related by the dual concepts of fuzzy implication and the compositional rule of inference. The FLC provides an algorithm which can convert the linguistic control strategy based on expert knowledge into an automatic control strategy. The concept of FLC is to utilize the qualitative knowledge of a system to design a practical controller. For a process control system, a fuzzy control algorithm embeds the intuition and experience of an operator designer and researcher. The fuzzy control method is suitable for systems with non-specific models, and therefore, it suits well to a process where the model is unknown or ill-defined and particularly to systems with uncertain or complex dynamics. The implementation of such control consists of translating the input variables to a language like: positive big, zero, negative small, etc. and to establish control rules so that the decision process can produce the appropriate outputs. Fuzzy Logic- Controls and Concepts introduces new concepts and theories of Fuzzy Logic Control for the application and development of robotics and intelligent machines. Fuzzy control (FC) using linguistic information possesses several advantages such as robustness, model-free, universal approximation theorem and rules-based algorithm. The inaccuracy and uncertainty are two aspects that may be part of the information. There are two theories used to deal with inaccuracy and uncertainty: classic sets (crisp) theory and probabilities theory, respectively. However, these theories do not always capture the information content provided by humans in natural language. The classic sets theory cannot deal with the fuzzy aspect of information while the probabilities theory is more suited to handle frequency information than those provided by humans. These theories have been used in systems that use human-provided information. These theories are closely linked with each other. When the fuzzy sets theory is used in a logic context, as knowledge-based systems, it is known as fuzzy logic (term used in this chapter). The fuzzy logic is currently one of the most successful technologies for the development of process control systems, due to low implementation cost, easy maintenance and the fact that complex requirements may be implemented in simple controllers.