Self‐recurrent wavelet neural network–based identification and adaptive predictive control of nonlinear dynamical systems |
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Authors: | Rajesh Kumar Smriti Srivastava JRP Gupta Amit Mohindru |
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Affiliation: | 1. Department of Instrumentation and Control Engineering, Bharati Vidyapeeth's College of Engineering, A‐4, Paschim Vihar, New Delhi‐ 110 2. 063, India;3. Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, New Delhi‐110 4. 078, India;5. Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology, New Delhi‐110 6. 020, India |
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Abstract: | In this paper, the problem of simultaneous identification and predictive control of nonlinear dynamical systems using self‐recurrent wavelet neural network (SRWNN) is addressed. The structure of the SRWNN is a modification of the wavelet neural network (WNN). Unlike WNN, the neurons present in the hidden layer of SRWNN contain the weighted self‐feedback loops. Dynamic back‐propagation algorithm is employed to derive the necessary parameter update equations. To further improve the convergence speed of the parameters, a time‐varying (adaptive) learning rate is used. Four simulation examples are considered for testing the effectiveness of the proposed method. Furthermore, some disturbance rejection tests are also performed on the proposed method. The results obtained through the simulation study confirm the effectiveness of the proposed method. |
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Keywords: | adaptive predictive control identification self‐recurrent wavelet neural network time‐varying learning rate |
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