Neuro-fuzzy variable structure control of robotic manipulators PDF Download

Author: Hasan Palaz
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Languages : tr
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Book Description
The main characteristics of Neural Network (NN) are to recognize patterns and to classify input, and to adapt themselves to dynamic environments by Iearning,but the mapping structure of NN in the intelligent controllers is a black box. it ispossible to understand what the box does, but not how it is done conceptually. In theother words, the resulting NN behavior in the control scheme is difficult tounderstand. In addition, Fuzzy Logic (FL) can cope with human knowledge and canperform inference, but FL does not fundamentally include Iearning mechanism.Neuro-fuzzy computing has developed for overcoming their disadvantages. Ingeneral, the NN part is used for Iearning, while fuzzy Iogic part is used forrepresenting knowledge. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) hasgreatly improved the realization performance of fuzzy system and has extensively been used for identification and control purposes.it is seen that the Variable Structure Control (VSC), NN and FL methodologies are complementary for control objectives and there can be much tobe gained in using them in a combined manner for the control of nonlinear systems.The resulting controller is a nonlinear one and may be suitable to overcome the difficulties involved in using conventional VSC for nonlinear systems. As the application area of neuro-fuzzy control expands from simple systems (in the sense that there are only a few key variables, and that Iarge number of trial-and-error experiments are permitted, Iike in washing machine, air conditioners, rice cookers,etc.) to more complex systems (in the sense that there are many key variables, and that unsuccessful trial-and-error experiments are not permitted, Iike in chemical processes, power plants, robotic systems, aircrafts, etc.), there is a urgent need for systematic design method of the neuro-fuzzy VSC systems which have the following properties,1. They alleviate the chattering phenomena while maintaining sliding behaviorwith accurate tracking performance,2. They have a self organizing capability that can start from an empty rule base,3. They produce an on-line controller that means the control and Iearning take splace simultaneously 4. They have a high degree robustness and fault tolerance,5. They are stable neuro-fuzzy control system,6. They introduce structured fuzzy concept that is easy to understand,7 .They visualize the evaluation of membership functions and the rule basesduring operation,8. They enable the designer to incorporate the experiences of control strategy into controller ,9. They have a self-tuning capability that can tune its parameters in on-line.The neuro-fuzzy approaches to VSC system in this doctoral dissertation tryto solve of this kind of problems. Roughly speaking, the following ideas areproposed. For the first five requirements, A Gaussian Neuro Fuzzy Variable Structure Control (GNFVSC) scheme is presented to tackle drawbacks ofconventional VSC. This controller consists of a Gaussian Radial Basis Function NN(RBFNN), which is considered as a smooth transition between FL and NN.For the first eight requirements, the adaptive neuro-fuzzy control system is introduced to estimate an equivalent control inside the boundary Iayer by incorporating Iinguistic control rules based on knowledge of the sliding surface variable into the controller .Finally , for the ninth requirement, a self tuning neuro-fuzzy V8C system is studied to eliminate fine tuning problem of the controller .