Self-recurrent wavelet neural network–based identification and adaptive predictive control of nonlinear dynamical systems
Corresponding Author
Rajesh Kumar
Department of Instrumentation and Control Engineering, Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, New Delhi- 110 063, India
Correspondence
Rajesh Kumar, Department of Instrumentation and Control Engineering, Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, New Delhi-110 063, India.
Email: [email protected]
Search for more papers by this authorSmriti Srivastava
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, New Delhi-110 078, India
Search for more papers by this authorJ.R.P Gupta
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, New Delhi-110 078, India
Search for more papers by this authorAmit Mohindru
Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology, New Delhi-110 020, India
Search for more papers by this authorCorresponding Author
Rajesh Kumar
Department of Instrumentation and Control Engineering, Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, New Delhi- 110 063, India
Correspondence
Rajesh Kumar, Department of Instrumentation and Control Engineering, Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, New Delhi-110 063, India.
Email: [email protected]
Search for more papers by this authorSmriti Srivastava
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, New Delhi-110 078, India
Search for more papers by this authorJ.R.P Gupta
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, New Delhi-110 078, India
Search for more papers by this authorAmit Mohindru
Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology, New Delhi-110 020, India
Search for more papers by this authorSummary
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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