Fuzzy Control Systems
CHAPTER I
1. Fuzzy control
Fuzzy control is a practical alternative for a variety of challenging control applicationssince it provides a convenient method for constructing nonlinear controllersvia the use of heuristic information. In a sense fuzzy systems can be “trained” and can “learn” how to perform throughout a control task and are considered as intelligent control system.
Fuzzy control system design is based on empirical methods, basically a methodical approach to trail-and-error. The general process is as follows: * Document the system’s operational specifications and inputs and outputs * Document the fuzzy sets for inputs * Document the rule set * Determine the defuzzification method * Run through test suite to validate systems, adjust details as required * Complete document and release to production
Fuzzy identification and control methods are used in many engineering systems. Aircraft flight control and navigation systems, which have traditionally used gain scheduling, are now increasingly employing methods of fuzzy control. Some automobile manufacturers use fuzzy logic to control automatic braking systems, transmissions, and suspension systems. In process control systems, fuzzy logic is used to control distillation columns and desalinization processes. In the field of robotics, fuzzy control is used to control end - effector position and path. At least one appliance manufacturer employs a fuzzy system to control turbidity in washing machine water, and at least one camera manufacturer ironically uses fuzzy logic in their autofocus system. 2. Fuzzy logic
Fuzzy logic provides a formal framework for constructing system exhibiting both good numeric performance (precision) and linguistic representation (interpretability). Fuzzy modeling—meaning the construction of fuzzy systems—is an arduous task, demanding the identification of many parameters. Fuzzy logic deals with uncertainty in engineering by attaching degrees of
1. Fuzzy control
Fuzzy control is a practical alternative for a variety of challenging control applicationssince it provides a convenient method for constructing nonlinear controllersvia the use of heuristic information. In a sense fuzzy systems can be “trained” and can “learn” how to perform throughout a control task and are considered as intelligent control system.
Fuzzy control system design is based on empirical methods, basically a methodical approach to trail-and-error. The general process is as follows: * Document the system’s operational specifications and inputs and outputs * Document the fuzzy sets for inputs * Document the rule set * Determine the defuzzification method * Run through test suite to validate systems, adjust details as required * Complete document and release to production
Fuzzy identification and control methods are used in many engineering systems. Aircraft flight control and navigation systems, which have traditionally used gain scheduling, are now increasingly employing methods of fuzzy control. Some automobile manufacturers use fuzzy logic to control automatic braking systems, transmissions, and suspension systems. In process control systems, fuzzy logic is used to control distillation columns and desalinization processes. In the field of robotics, fuzzy control is used to control end - effector position and path. At least one appliance manufacturer employs a fuzzy system to control turbidity in washing machine water, and at least one camera manufacturer ironically uses fuzzy logic in their autofocus system. 2. Fuzzy logic
Fuzzy logic provides a formal framework for constructing system exhibiting both good numeric performance (precision) and linguistic representation (interpretability). Fuzzy modeling—meaning the construction of fuzzy systems—is an arduous task, demanding the identification of many parameters. Fuzzy logic deals with uncertainty in engineering by attaching degrees of