Time series predictive wavelet neural network control method

Vector time series autoregressive moving average predictive and wavelet neural networks PID method is proposed in order to solve the requirement of optimal parameters real-time calculation in various control fields (engineering systems, economics systems, etc.), especially to solve the instability and poor control performance problem of control parameters online-tuning in engineering practice. Based on additional wavelet neural networks and vector autoregressive moving average predict method and comparative experiments of relative methods, better feasibility, reliability, speed, lower static error, more flexible parameter adjustment ability are verified.

[1]  Maolin Jin,et al.  Design of an online tuning modified-grey fuzzy PID controller for nonlinear systems , 2011, Proceedings of 2011 International Conference on Fluid Power and Mechatronics.

[2]  M. Sabahi,et al.  Self-adaptive RBF neural network PID controller in linear elevator , 2011, 2011 International Conference on Electrical Machines and Systems.

[3]  Stuart Bennett,et al.  The past of pid controllers , 2000, Annu. Rev. Control..

[4]  Feng Dong-qing,et al.  Design and Research Based on Fuzzy PID-Parameters Self-Tuning Controller with MATLAB , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[5]  Wenchao Li,et al.  Based on the fuzzy PID brushless DC motor control system design , 2012, Proceedings of 2012 International Conference on Measurement, Information and Control.

[6]  Hongbo Shi,et al.  A BP Wavelet Neural Network Structure for Process Monitoring and Fault Detection , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[7]  Bernard Delyon,et al.  Accuracy analysis for wavelet approximations , 1995, IEEE Trans. Neural Networks.

[8]  Tyrone Fernando,et al.  Simulation of fuzzy-modified expert PID algorithms for blood glucose control , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[9]  Hong Chen,et al.  Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks , 1993, IEEE Trans. Neural Networks.

[10]  K.M. Vu.  The Moments of a Vector Autoregressive Moving Average Time Series , 2007, 2007 American Control Conference.