基于未建模动态补偿的非线性自适应切换控制方法

柴天佑; 张亚军

doi:10.3724/SP.J.1004.2011.00773

基于未建模动态补偿的非线性自适应切换控制方法

doi: 10.3724/SP.J.1004.2011.00773

柴天佑^1,2,
张亚军¹

1.
流程工业综合自动化国家重点实验室(东北大学) 沈阳 110004;
2.
东北大学自动化研究中心沈阳 110004

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出版历程
- 收稿日期: 2010-10-15
- 修回日期: 2011-01-22
- 刊出日期: 2011-07-20

Nonlinear Adaptive Switching Control Method Based on Unmodeled Dynamics Compensation

CHAI Tian-You^1,2,
ZHANG Ya-Jun¹

1.
State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Shenyang 110004;
2.
Research Center of Automation, Northeastern University, Shenyang 110004

摘要

摘要: 针对一类不确定的离散时间零动态不稳定的单输入-单输出(Single-input single-output, SISO)非线性系统,提出了一种基于未建模动态补偿的非线性控制器. 采用自适应神经模糊推理系统(Adaptive-network-based fuzzy inference system, ANFIS)和一一映射相结合的方法估计未建模动态.在此基础上,提出了由线性自适应控制器、非线性自适应控制器以及切换机制组成的自适应切换控制方法.该方法通过对上述两种控制器的切换, 保证闭环系统输入输出信号有界的同时,改善系统性能.本文将要求未建模动态全局有界的条件放宽为线性增长, 建立了所提自适应控制方法的稳定性和收敛性分析.通过仿真比较和水箱的液位控制实验,验证了所提方法的有效性.
- 非线性系统 /
- 不稳定零动态 /
- 未建模动态补偿 /
- 自适应切换控制
Abstract: This paper presents a nonlinear controller based on unmodeled dynamics compensation for a class of uncertain and discrete-time single-input single-output (SISO) nonlinear systems with unstable zero-dynamics. By combining an adaptive-network-based fuzzy inference system (ANFIS) with "one-to-one mapping", a compensator for unmodeled dynamics is constructed. With the above development, an adaptive switching control method is proposed that consists of a linear adaptive controller, a nonlinear adaptive controller and a switching mechanism. By using switching between the above two controllers, it has been shown that both an improved performance and stability can be achieved simultaneously. The paper assumes the unmodeled dynamics of the systems to satisfy a linear growth condition, which relaxes the widely used global boundedness condition on the unmodeled dynamics. The analysis on stability and convergence of the adaptive control method are established. Finally, through the simulation based comparative study and the experiment of the proposed control on a tank level adaptive control system, the effectiveness of the proposed method is justified.
- Nonlinear system /
- unstable zero dynamics /
- unmodeled dynamics compensation /
- adaptive switching control

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参考文献(1)

[1]

Chen L J, Narendra K S. Nonlinear adaptive control using neural networks and multiple models. Automatica, 2001, 37(8): 1245-1255[2] Fu Y, Chai T Y, Yue H. Intelligent control using multiple models and neural networks. International Journal of Adaptive Control and Signal Processing, 2007, 22(5): 495-509[3] Shi Yu-Jing, Chai Tian-You. Neural networks and multiple models based nonlinear adaptive generalized predictive control. Acta Automatica Sinica, 2007, 33(5): 540-545(石宇静, 柴天佑. 基于神经网络与多模型的非线性自适应广义预测控制. 自动化学报, 2007, 33(5): 540-545)[4] Zhang Ya-Jun, Chai Tian-You, Fu Yue. Adaptive control method for a class of nonlinear systems based on ANFIS and multiple models. Journal of Chemical Industry and Engineering, 2010, 61(8): 2084-2091(张亚军, 柴天佑, 富月. 基于ANFIS与多模型的一类非线性系统的自适应控制方法, 化工学报, 2010, 61(8): 2084-2091)[5] Fu Y, Chai T Y. Nonlinear multivariable adaptive control using multiple models and neural networks. Automatica, 2007, 43(6): 1101-1110[6] Tong S C, He X L, Zhang H G. A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control. IEEE Transactions on Fuzzy Systems, 2009, 17(5): 1059-1069[7] Sanner R M, Slotine J J E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 1992, 3(6): 837-863[8] Jang J S R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on System, Man, and Cybernetics, 1993, 23(3): 665-685[9] Wang H, Wang A P, Brown M, Harris C J. One to one mapping and its application to neural networks based control systems design. International Journal of System Science, 1996, 27(2): 161-170[10] Chen L J, Narendra K S. Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework. IEEE Transactions on Neural Networks, 2004, 15(3): 663-673[11] Middleton R H, Goodwin G C, Hill D J, Mayne D Q. Design issues in adaptive control. IEEE Transactions on Automatic Control, 1988, 33(1): 50-58[12] Xie Xue-Jun, Zhang Da-Lei. Adaptive control of nonlinear discrete-time systems using radial basis function networks. Acta Automatica Sinica, 2000, 26(3): 414-418(解学军, 张大雷. 基于径向基函数网络的非线性离散时间系统的自适应控制. 自动化学报, 2000, 26(3): 414-418)[13] Fu Y, Chai T Y. Neural-network-based nonlinear adaptive dynamical decoupling control. IEEE Transactions on Neural Networks, 2007, 18(3): 921-925[14] Takagi K, Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116-132[15] Sugeno M, Kang G T. Structure identification of fuzzy model. Fuzzy Sets and Systems, 1988, 28(1): 15-33 [16] Zhang H G, Quan Y B. Modeling, identification, and control of a class of nonlinear system. IEEE Transactions on Fuzzy Systems, 2001, 9(2): 349-354[17] Shu Di-Qian, Rao Li-Chang, Chai Tian-You. Adaptive Control. Shenyang: Northeastern University Press, 1992(舒迪前, 饶立昌, 柴天佑. 自适应控制, 沈阳: 东北大学出版社, 1992)[18] Goodwin G C, Ramadge P J, Caines P E. Discrete-time multivariable adaptive control. IEEE Transactions on Automatic Control, 1980, 25(3): 449-456

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