具有未建模动态和输出约束的耦合系统的分散自适应控制
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摘要
针对一类具有输入、状态未建模动态和非线性输入的耦合系统,提出一种自适应神经网络控制方案.利用径向基函数神经网络逼近未知非线性连续函数;引入动态信号和正则化信号处理状态及输入未建模动态;通过引入非线性映射,将具有时变输出约束的严格反馈系统化为不含约束的严格反馈系统.最后,通过理论分析验证闭环系统中所有信号是半全局一致最终有界的,仿真结果进一步验证了所提出控制方案的有效性.
An adaptive neural network control scheme is proposed for a class of interconnected systems with state and input unmodeled dynamics as well as input nonlinearity. The unknown nonlinear continuous functions are approximated by radial basis function neural networks(RBFNNs). State and input unmodeled dynamics are dealt with by introducing dynamic signal and normalization signal. The strict-feedback system with time-varying output constraint is transformed into a novel strict-feedback system without constraint by introducing one to one nonlinear mapping. By theoretical analysis, all the signals in the closed-loop control system are proved to be semi-globally uniformly ultimately bounded.Simulation results show the effectiveness of the proposed approach.
An adaptive neural network control scheme is proposed for a class of interconnected systems with state and input unmodeled dynamics as well as input nonlinearity. The unknown nonlinear continuous functions are approximated by radial basis function neural networks(RBFNNs). State and input unmodeled dynamics are dealt with by introducing dynamic signal and normalization signal. The strict-feedback system with time-varying output constraint is transformed into a novel strict-feedback system without constraint by introducing one to one nonlinear mapping. By theoretical analysis, all the signals in the closed-loop control system are proved to be semi-globally uniformly ultimately bounded.Simulation results show the effectiveness of the proposed approach.
引文
[1] Zhang T P, Feng C B. Decentralized adaptive fuzzy control for large-scale nonlinear systems[J]. Fuzzy Sets and Systems, 1997, 92(1):61-70.
[2] Zhang T P. Stable direct adaptive fuzzy control for a class of MIMO nonlinear systems[J]. Int J of Systems Science,2003, 92(1):375-388.
[3] Xia X N, Zhang T P, Wang Q. Decentralized adaptive output feedback dynamic surface control of interconnected nonlinear systems with unmodeled dynamics[J]. J of The Franklin Institute, 2015, 352(3):1031-1055.
[4] Li H, Niu B, Zhang L. Adaptive backstepping based neural decentralized control for stochastic switched systems[C]. Proc of the 31st Youth Academic Annual Conf on Chinese Association of Automation(YAC).Wuhan, 2016:261-266.
[5] Wang H Q, Liu W X, Qiu J B, et al. Adaptive fuzzy decentralized control for a class of strong interconnected nonlinear systems with unmodeled dynamics[J]. IEEE Trans on Fuzzy Systems, 2017, 99:1-11, DOI:10.1109/TFUZZ.2017.2694799.
[6] Wang H Q, Liu W X, Liu X P, et al. Adaptive fuzzy decentralized control for a class of interconnected nonlinear system with unmodeled dynamics and dead zones[J]. Neurocomputing, 2016, 214:972-980.
[7] Liu L, Xie X J. Decentralized adaptive stabilization for interconnected systems with dynamic input-output and nonlinear interaction[J]. Automatica, 2010, 46(6):1060-1067.
[8] Jiang Z P, Praly L. Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties[J].Automatica, 1998, 34(7):825-840.
[9] Jiang Z P, Hill D J. A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics[J]. IEEE Trans on Automatic Control, 1999, 44(9):1705-1711.
[10] Mao J, Xiang Z R, Huang S P. Adaptive witched nonlinear systems with unmodeled dynamics[J]. Neurocomputing,2016, 196:42-52.
[11] Krstic M, Kokotovic P V. Robust control of nonlinear systems with input unmodeled dynamics[J]. Systems and Control Letters, 2000, 41(2):115-122.
[12] Krstic M, Sun J, Kokotovic P V. Robust control of nonlinear systems with input unmodeled dynamics[J].IEEE Trans on Automatic Control, 1996, 41(6):913-920.
[13] Xia X N, Zhang T P, Yi Y, et al. Adaptive prescribed performance control of output feedback systems including input unmodeled dynamics[J]. Neurocomputing, 2016,190:226-236.
[14] Xia X N, Zhang T P. Adaptive reduced-order K-filters based DSC of uncertain systems with input unmodeled dynamics and dead zone[C]. Proc of the 35th Chinese Control Conf. Piscataway:Institute of Electrical and Electronics Engineers Inc, 2016:463-468.
[15] Ngo K B, Mahony R, Jiang Z P. Integrator backstepping using barrier functions for systems with multiple state constraints[C]. Proc of the 44th IEEE Conf on Decision and Control. Piscataway:Institute of Electrical and Electronics Engineers Inc, 2005:8306-8312.
[16] Tee K P, Ge S S, Tay E H. Barrier Lyapunov functions for the control of output-constrained nonlinear systems[J].Automatica, 2009, 45(4):918-927.
[17] Ren B B, Ge S S, Tee K P, et al. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function[J]. IEEE Trans on Neural Networks,2010, 21(8):1339-1345.
[18] Tee K P, Ren B B, Ge S S. Control of nonlinear systems with time-varying output constraints[J]. Automatica,2011, 47(11):2511-2516.
[19] Liu L, Yang X B. Robust adaptive state constraint control for uncertain switched high-order nonlinear systems[J].IEEE Trans on Industrial Electronics, 2017, DOI:10.1109/TIE.2017.2701774.
[20] Zhang T P, Xia M Z, Yi Y. Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics[J].Automatica, 2017, 81:232-239.
[21] Meng W C, Yang Q M, Sun Y X. Adaptive neural control of nonlinear MIMO systems with time-varying output constraints[J]. IEEE Trans on Neural Network and Learning Systems, 2015, 26(5):1074-1085.
[22] Ge S S, Wang C. Direct adaptive NN control of class of nonlinear systems[J]. IEEE Trans on Neural Networks,2002, 13(1):214-221.
[23] Xia X N, Zhang T P. Adaptive output feedback control of nonlinear systems with unmodeled dynamics and prescribed tracking performance[C]. Proc of the 34th Chinese Control Conf. Piscataway:Institute of Electrical and Electronics Engineers Inc, 2015:2983-2988.
[24] Zhang T P, Zhu Q, Yi Yang, Adaptive neural control nonaffine pure-feedback nonlinear systems with input nonlinearity and perturbed uncertainties[J]. Int J of Systems Science, 2012, 34(4):375-388.
[25] Mehraeen S, Jagannathan S, Crow M L. Decentralized dynamic surface control of large-scale interconnected systems in strict-feedback form using neural networks with asymptotic stabilization[J]. IEEE Trans on Neural Networks, 2011, 22(11):1709-1722.
[26] Lin W, Qian C J. Adaptive control of nonlinearly parameterized systems:The smooth feedback case[J].IEEE Trans on Automatic Control, 2002, 47(8):1249-1266.
[27] Ye X D, Jiang Z P. Adaptive nonlinear design without a priori knowledge of control directions[J]. IEEE Trans on Automatic Control, 1998, 43(11):1617-1621.
[28] Liu Y J, Tong S C, Li T S. Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems[J]. Fuzzy Sets and Systems, 2011, 164(1):25-44.
[29] Huang Y S, Wu M. Robust decentralized direct adaptive output feedback fuzzy control for a class of large-scale non-affine nonlinear systems[J]. Information Sciences,2014, 181(11):2392-2404.
[2] Zhang T P. Stable direct adaptive fuzzy control for a class of MIMO nonlinear systems[J]. Int J of Systems Science,2003, 92(1):375-388.
[3] Xia X N, Zhang T P, Wang Q. Decentralized adaptive output feedback dynamic surface control of interconnected nonlinear systems with unmodeled dynamics[J]. J of The Franklin Institute, 2015, 352(3):1031-1055.
[4] Li H, Niu B, Zhang L. Adaptive backstepping based neural decentralized control for stochastic switched systems[C]. Proc of the 31st Youth Academic Annual Conf on Chinese Association of Automation(YAC).Wuhan, 2016:261-266.
[5] Wang H Q, Liu W X, Qiu J B, et al. Adaptive fuzzy decentralized control for a class of strong interconnected nonlinear systems with unmodeled dynamics[J]. IEEE Trans on Fuzzy Systems, 2017, 99:1-11, DOI:10.1109/TFUZZ.2017.2694799.
[6] Wang H Q, Liu W X, Liu X P, et al. Adaptive fuzzy decentralized control for a class of interconnected nonlinear system with unmodeled dynamics and dead zones[J]. Neurocomputing, 2016, 214:972-980.
[7] Liu L, Xie X J. Decentralized adaptive stabilization for interconnected systems with dynamic input-output and nonlinear interaction[J]. Automatica, 2010, 46(6):1060-1067.
[8] Jiang Z P, Praly L. Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties[J].Automatica, 1998, 34(7):825-840.
[9] Jiang Z P, Hill D J. A robust adaptive backstepping scheme for nonlinear systems with unmodeled dynamics[J]. IEEE Trans on Automatic Control, 1999, 44(9):1705-1711.
[10] Mao J, Xiang Z R, Huang S P. Adaptive witched nonlinear systems with unmodeled dynamics[J]. Neurocomputing,2016, 196:42-52.
[11] Krstic M, Kokotovic P V. Robust control of nonlinear systems with input unmodeled dynamics[J]. Systems and Control Letters, 2000, 41(2):115-122.
[12] Krstic M, Sun J, Kokotovic P V. Robust control of nonlinear systems with input unmodeled dynamics[J].IEEE Trans on Automatic Control, 1996, 41(6):913-920.
[13] Xia X N, Zhang T P, Yi Y, et al. Adaptive prescribed performance control of output feedback systems including input unmodeled dynamics[J]. Neurocomputing, 2016,190:226-236.
[14] Xia X N, Zhang T P. Adaptive reduced-order K-filters based DSC of uncertain systems with input unmodeled dynamics and dead zone[C]. Proc of the 35th Chinese Control Conf. Piscataway:Institute of Electrical and Electronics Engineers Inc, 2016:463-468.
[15] Ngo K B, Mahony R, Jiang Z P. Integrator backstepping using barrier functions for systems with multiple state constraints[C]. Proc of the 44th IEEE Conf on Decision and Control. Piscataway:Institute of Electrical and Electronics Engineers Inc, 2005:8306-8312.
[16] Tee K P, Ge S S, Tay E H. Barrier Lyapunov functions for the control of output-constrained nonlinear systems[J].Automatica, 2009, 45(4):918-927.
[17] Ren B B, Ge S S, Tee K P, et al. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function[J]. IEEE Trans on Neural Networks,2010, 21(8):1339-1345.
[18] Tee K P, Ren B B, Ge S S. Control of nonlinear systems with time-varying output constraints[J]. Automatica,2011, 47(11):2511-2516.
[19] Liu L, Yang X B. Robust adaptive state constraint control for uncertain switched high-order nonlinear systems[J].IEEE Trans on Industrial Electronics, 2017, DOI:10.1109/TIE.2017.2701774.
[20] Zhang T P, Xia M Z, Yi Y. Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics[J].Automatica, 2017, 81:232-239.
[21] Meng W C, Yang Q M, Sun Y X. Adaptive neural control of nonlinear MIMO systems with time-varying output constraints[J]. IEEE Trans on Neural Network and Learning Systems, 2015, 26(5):1074-1085.
[22] Ge S S, Wang C. Direct adaptive NN control of class of nonlinear systems[J]. IEEE Trans on Neural Networks,2002, 13(1):214-221.
[23] Xia X N, Zhang T P. Adaptive output feedback control of nonlinear systems with unmodeled dynamics and prescribed tracking performance[C]. Proc of the 34th Chinese Control Conf. Piscataway:Institute of Electrical and Electronics Engineers Inc, 2015:2983-2988.
[24] Zhang T P, Zhu Q, Yi Yang, Adaptive neural control nonaffine pure-feedback nonlinear systems with input nonlinearity and perturbed uncertainties[J]. Int J of Systems Science, 2012, 34(4):375-388.
[25] Mehraeen S, Jagannathan S, Crow M L. Decentralized dynamic surface control of large-scale interconnected systems in strict-feedback form using neural networks with asymptotic stabilization[J]. IEEE Trans on Neural Networks, 2011, 22(11):1709-1722.
[26] Lin W, Qian C J. Adaptive control of nonlinearly parameterized systems:The smooth feedback case[J].IEEE Trans on Automatic Control, 2002, 47(8):1249-1266.
[27] Ye X D, Jiang Z P. Adaptive nonlinear design without a priori knowledge of control directions[J]. IEEE Trans on Automatic Control, 1998, 43(11):1617-1621.
[28] Liu Y J, Tong S C, Li T S. Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems[J]. Fuzzy Sets and Systems, 2011, 164(1):25-44.
[29] Huang Y S, Wu M. Robust decentralized direct adaptive output feedback fuzzy control for a class of large-scale non-affine nonlinear systems[J]. Information Sciences,2014, 181(11):2392-2404.
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