A HIERARCHICAL FUZZY-NEURAL MULTI-MODEL An application for a mechanical system with friccion identifcation and control
نویسندگان
چکیده
A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation leamng method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of two control schemes, which are: a trajectory tracking control by an inverse FNHMM and a direct adaptive control, using the states issued by the identification FNHMM. The proposed control rnethods are applied for a mechanical plant with fiiction system control, where the obtained comparative results show that the control using FNHMM outperforms the fuzzy and the neural single control.
منابع مشابه
A hierarchical fuzzy-neural multi-model: an application for a mechanical system with friccion identification and control
A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of t...
متن کاملMulti-Criteria Risk-Benefit Analysis of Health Care Management
Abstract Purpose of this paper: The objectives of this paper are two folds: (1) utilizing hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach to evaluate the most suitable RFID-based systems decision, and (2) to highlight key risks and benefits of radio frequency identification technology in healthcare industry. Design/methodology/approach: R...
متن کاملModel-based Approach for Multi-sensor Fault Identification in Power Plant Gas Turbines
In this paper, the multi-sensor fault diagnosis in the exhaust temperature sensors of a V94.2 heavy duty gas turbine is presented. A Laguerre network-based fuzzy modeling approach is presented to predict the output temperature of the gas turbine for sensor fault diagnosis. Due to the nonlinear dynamics of the gas turbine, in these models the Laguerre filter parts are related to the linear d...
متن کاملTrajectory tracking of under-actuated nonlinear dynamic robots: Adaptive fuzzy hierarchical terminal sliding-mode control
In recent years, underactuated nonlinear dynamic systems trajectory tracking, such as space robots and manipulators with structural flexibility, has become a major field of interest due to the complexity and high computational load of these systems. Hierarchical sliding mode control has been investigated recently for these systems; however, the instability phenomena will possibly occur, especia...
متن کاملA TS Fuzzy Model Derived from a Typical Multi-Layer Perceptron
In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modify...
متن کامل