Research on Improvement Algorithm of Model Free Learning Adaptive Controller

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To improve the convergence rate of model-free learning adaptive controller (MFLAC) and decrease difficulty of parameters choice in control law, a new design method of MFLAC is presented. The controller proposed in this paper is designed based on pseudo gradient concept with solution by introducing multi-innovation method. In order to illustrate the effectiveness of the proposed method,we have given an example.

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1500-1504

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February 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] K. J. Ästöm and T. Hägglund, PID Controllers: theory, design and tuning, Instrument Society of America, ISA, (1995).

Google Scholar

[2] Zhongsheng Hou and Wenhu Huang, The Model-free Learning Adaptive Control of a Class of SISO Nonlinear Systems, Proceddings of the American Control Conference, Albuquerque, pp.343-344, (1997).

DOI: 10.1109/acc.1997.611815

Google Scholar

[3] Zhongsheng Hou, C. Han and Wenhu Huang, The Model-free Learning Adaptive Control of a Class of MISO Nonlinear Discrete-time Systems, IFAC Low Cost Automation, Shenyang, P.R. China, pp.227-232, (1998).

DOI: 10.1016/s1474-6670(17)36391-7

Google Scholar

[4] Zhongsheng Hou and Zhigang Han, Robust Modelless Learning Adaptive Control of Nonlinear Systems, Control and Decision, vol. 10, no. 2, pp.137-142, (1995).

Google Scholar

[5] Tiezhu Zhang, Renxue Song and Zhigang Han, Universal Model Method of Linearization of Discrete Time Nonlinear System, Control and Decision, vol. 17, no. 2, pp.249-251, (2002).

Google Scholar

[6] Lcandro dos Santos Coclho, Antonio Augusto Rodrigucs Coclho and Rodrigo R. Sumar, Model-free Learning Adaptive Controller with Neural Network Compensator and Differential Evolution Optimization, Proc. of the 2006 IEEE international Symposium on intelligent Control Munich, Germany, pp.2018-2023, (2006).

DOI: 10.1109/isic.2006.285646

Google Scholar

[7] Leandro dos Santos Coelho and Fabio A. Guerra, Appling Particle Swarm Optimization to Adaptive Controller, Soft Computing in Industrial Applications, ASC39, pp.82-91, (2007).

DOI: 10.1007/978-3-540-70706-6_8

Google Scholar

[8] Feng Ding and Tongwen Chen, Performance Analysis of Multi-innovation Gradient Type Identification Methods, Automatica, vol. 43, pp.1-14, (2007).

DOI: 10.1016/j.automatica.2006.07.024

Google Scholar

[9] L. Ljung, System identification: Theory for the User (2nd ed. ), Englewood Cliffs, NJ: Pentice-Hall, (1999).

Google Scholar

[10] Feng Ding, Tongwen Chen, Multi-innovation stochastic gradient identification methods. Proceedings of the sixth world congress on intelligent control and automation (WCICA2006), June 21-23, 2006, Dalian, China, pp.1501-1505.

DOI: 10.1109/wcica.2006.1712600

Google Scholar

[11] Feng Ding, Tao Ding, Mean Square Convergence of Multi-innovation Projection Algorithm for Time-varying Multivariable Systems, journal of Hubei polytechnic university, vol. 16, no. 4, pp.16-20, (2001).

Google Scholar

[12] Xiaolei Li, Zhijiang Shao and Jixin Qian, An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm, Systems Engineering-theory & Practice, no. 11, pp.32-38, (2002).

Google Scholar

[13] Xiaolei Li, Shaohui Feng, Jixin Qian and Fei Lu, Parameter Tuning Method of Robust PID Controller Based on Artificial Fish School Algorithm, Infromation and Control, Vol. 33, no. 1, pp.112-115, (2004).

Google Scholar

[14] T. Wigren, Recursive prediction error identification using the nonlinear Wiener model, Automatica, vol. 29, no. 4, pp.1011-1025, (1993).

DOI: 10.1016/0005-1098(93)90103-z

Google Scholar