Neural Adaptive Dynamic Surface Control for Mismatched Uncertain Nonlinear Systems with Nonlinear Feedback Errors
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- 英文论文题名：Neural Adaptive Dynamic Surface Control for Mismatched Uncertain Nonlinear Systems with Nonlinear Feedback Errors
- 论文作者：Shigen Gao ; Hairong Dong ; Bin Ning ; Tao Tang ; Yidong Li ; Kimon P.Valavanis
- 英文论文作者：Shigen Gao ; Hairong Dong ; Bin Ning ; Tao Tang ; Yidong Li ; Kimon P.Valavanis ; State Key Laboratory of Rail Traffic Control and Safety ; Beijing Jiaotong University ; School of Computer and Information Technology ; Beijing Jiaotong University ; Department of Electrical and Computer Engineering ; School of Engineering and Computer Science ; University of Denver
- 年：2017
- 作者机构：State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University;School of Computer and Information Technology,Beijing Jiaotong University;Department of Electrical and Computer Engineering,School of Engineering and Computer Science,University of Denver;
- 英文论文关键词：Dynamic Surface Control ; Neural Adaptive Control ; Nonlinear Gain Feedback
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：O231
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：337k
- 原文格式：D
- 会议级别：全国
摘要
This paper addresses a new nonlinear gain feedback based neural adaptive dynamic surface control(DSC) method for a class of strict feedback nonlinear systems in the presence of uncertainties and external disturbances. Besides circumventing the problem of complexity explosion, the nonlinear gain feedback technique brings a self-regulation ability into the pioneering DSC method to improve the dynamic performance of the resulted closed-loop system. Neural networks are used to online approximating the uncertainties, the over-fitting problem occurs with small probability since the signals obtained by NNs are also handled using nonlinear gain function. It is proved rigorously by Lyapunov stability theorem that all the closed-loop signals are kept semi-globally uniformly ultimately bounded, the output tracking error and the optimal neural weight vector estimation error can all be adjusted to arbitrarily small around zero characterized by design parameters in an explicit way. Furthermore,the method proposed can be extended to the control of multiple-input-multiple-output system trivially. Comparative simulation results are presented to demonstrate the effectiveness of the proposed method.
This paper addresses a new nonlinear gain feedback based neural adaptive dynamic surface control(DSC) method for a class of strict feedback nonlinear systems in the presence of uncertainties and external disturbances. Besides circumventing the problem of complexity explosion, the nonlinear gain feedback technique brings a self-regulation ability into the pioneering DSC method to improve the dynamic performance of the resulted closed-loop system. Neural networks are used to online approximating the uncertainties, the over-fitting problem occurs with small probability since the signals obtained by NNs are also handled using nonlinear gain function. It is proved rigorously by Lyapunov stability theorem that all the closed-loop signals are kept semi-globally uniformly ultimately bounded, the output tracking error and the optimal neural weight vector estimation error can all be adjusted to arbitrarily small around zero characterized by design parameters in an explicit way. Furthermore,the method proposed can be extended to the control of multiple-input-multiple-output system trivially. Comparative simulation results are presented to demonstrate the effectiveness of the proposed method.
This paper addresses a new nonlinear gain feedback based neural adaptive dynamic surface control(DSC) method for a class of strict feedback nonlinear systems in the presence of uncertainties and external disturbances. Besides circumventing the problem of complexity explosion, the nonlinear gain feedback technique brings a self-regulation ability into the pioneering DSC method to improve the dynamic performance of the resulted closed-loop system. Neural networks are used to online approximating the uncertainties, the over-fitting problem occurs with small probability since the signals obtained by NNs are also handled using nonlinear gain function. It is proved rigorously by Lyapunov stability theorem that all the closed-loop signals are kept semi-globally uniformly ultimately bounded, the output tracking error and the optimal neural weight vector estimation error can all be adjusted to arbitrarily small around zero characterized by design parameters in an explicit way. Furthermore,the method proposed can be extended to the control of multiple-input-multiple-output system trivially. Comparative simulation results are presented to demonstrate the effectiveness of the proposed method.
引文
[1]S.Gao,H.Dong,Y.Chen,B.Ning,G.Chen,and X.Yang,“Approximation-based robust adaptive automatic train control:An approach for actuator saturation,”IEEE Transactions on Intelligent Transportation Systems,vol.14,no.4,pp.1733–1742,2013.
[2]S.Gao,H.Dong,B.Ning,Y.Chen,and X.Sun,“Adaptive fault-tolerant automatic train operation using rbf neural networks,”Neural Computing and Applications,vol.26,pp.141–149,2015.
[3]S.Gao,H.Dong,B.Ning,C.Roberts,and L.Chen,“Neural adaptive coordination control of multiple trains under bidirectional communication topology,”Neural Computing and Applications,vol.27,no.8,pp.2497–2507,2016.
[4]P.P.Yip and J.K.Hedrick,“Adaptive dynamic surface control:A simplified algorithm for adaptive backstepping control of nonlinear systems,”International Journal of Control,vol.71,no.5,pp.959–979,2010.
[5]S.Gao,H.Dong,B.Ning,and X.Yao,“Single-parameterlearning-based fuzzy fault-tolerant output feedback dynamic surface control of constrained-input nonlinear systems,”Information Sciences,vol.385,pp.378–394,2017.
[6]D.Swaroop,J.K.Hedrick,P.P.Yip,and J.C.Gerdes,“Dynamic surface control for a class of nonlinear systems,”IEEE Transactions on Automatic Control,vol.45,no.10,pp.1893–1899,2000.
[7]Z.Peng,D.Wang,Z.Chen,X.Hu,and W.Lan,“Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics,”IEEE Transactions on Control Systems Technology,vol.21,no.2,pp.513–520,2013.
[8]M.Wang and C.Wang,“Learning from adaptive neural dynamic surface control of strict-feedback systems,”IEEE transactions on neural networks and learning systems,vol.26,no.6,pp.1247–1259,2015.
[9]S.Gao,B.Ning,and H.Dong,“Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems,”Neural Computing and Applications,pp.1849–1857,2015.
[10]Y.Pan and H.Yu,“Dynamic surface control via singular perturbation analysis,”Automatica,vol.57,pp.29–33,2015.
[11]Z.Lin,M.Pachter,and S.Banda,“Toward improvement of tracking performance nonlinear feedback for linear systems,”International Journal of Control,vol.70,no.1,pp.1–11,1998.
[12]P.V.Kokotovic and R.Marino,“On vanishing stability regions in nonlinear systems with high-gain feedback,”IEEE Transactions on Automatic Control,vol.83,no.10,pp.967–970,1986.
[13]C.Kwan and F.L.Lewis,“Robust backstepping control of nonlinear systems using neural networks,”IEEE Transactions on Systems,Man,and Cybernetics,Part A Systems&Humans,vol.30,no.6,pp.753–766,2000.
[14]P.A.Ioannou and J.Sun,Robust adaptive control.Courier Corporation,2012.
[15]D.Wang and J.Huang,“Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form,”IEEE Transactions on Neural Networks,vol.16,no.1,pp.195–202,2005.
[16]S.Gao,H.Dong,B.Ning,and X.Sun,“Neural adaptive control for uncertain mimo systems with constrained input via intercepted adaptation and single learning parameter approach,”Nonlinear Dynamics,vol.82,no.3,pp.1109–1126,2015.
[17]S.Gao,H.Dong,S.Lyu,and B.Ning,“Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation,”International Journal of Control,vol.17,no.8,pp.1447–1466,2016.
[18]S.Gao,H.Dong,B.Ning,and L.Chen,“Neural adaptive control for uncertain nonlinear system with input saturation:State transformation based output feedback,”Neurocomputing,vol.159,pp.117–125,2015.
[19]S.Gao,B.Ning,and H.Dong,“Fuzzy dynamic surface control for uncertain nonlinear systems under input saturation via truncated adaptation approach,”Fuzzy Sets&Systems,vol.290,pp.100–117,2016.
[2]S.Gao,H.Dong,B.Ning,Y.Chen,and X.Sun,“Adaptive fault-tolerant automatic train operation using rbf neural networks,”Neural Computing and Applications,vol.26,pp.141–149,2015.
[3]S.Gao,H.Dong,B.Ning,C.Roberts,and L.Chen,“Neural adaptive coordination control of multiple trains under bidirectional communication topology,”Neural Computing and Applications,vol.27,no.8,pp.2497–2507,2016.
[4]P.P.Yip and J.K.Hedrick,“Adaptive dynamic surface control:A simplified algorithm for adaptive backstepping control of nonlinear systems,”International Journal of Control,vol.71,no.5,pp.959–979,2010.
[5]S.Gao,H.Dong,B.Ning,and X.Yao,“Single-parameterlearning-based fuzzy fault-tolerant output feedback dynamic surface control of constrained-input nonlinear systems,”Information Sciences,vol.385,pp.378–394,2017.
[6]D.Swaroop,J.K.Hedrick,P.P.Yip,and J.C.Gerdes,“Dynamic surface control for a class of nonlinear systems,”IEEE Transactions on Automatic Control,vol.45,no.10,pp.1893–1899,2000.
[7]Z.Peng,D.Wang,Z.Chen,X.Hu,and W.Lan,“Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics,”IEEE Transactions on Control Systems Technology,vol.21,no.2,pp.513–520,2013.
[8]M.Wang and C.Wang,“Learning from adaptive neural dynamic surface control of strict-feedback systems,”IEEE transactions on neural networks and learning systems,vol.26,no.6,pp.1247–1259,2015.
[9]S.Gao,B.Ning,and H.Dong,“Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems,”Neural Computing and Applications,pp.1849–1857,2015.
[10]Y.Pan and H.Yu,“Dynamic surface control via singular perturbation analysis,”Automatica,vol.57,pp.29–33,2015.
[11]Z.Lin,M.Pachter,and S.Banda,“Toward improvement of tracking performance nonlinear feedback for linear systems,”International Journal of Control,vol.70,no.1,pp.1–11,1998.
[12]P.V.Kokotovic and R.Marino,“On vanishing stability regions in nonlinear systems with high-gain feedback,”IEEE Transactions on Automatic Control,vol.83,no.10,pp.967–970,1986.
[13]C.Kwan and F.L.Lewis,“Robust backstepping control of nonlinear systems using neural networks,”IEEE Transactions on Systems,Man,and Cybernetics,Part A Systems&Humans,vol.30,no.6,pp.753–766,2000.
[14]P.A.Ioannou and J.Sun,Robust adaptive control.Courier Corporation,2012.
[15]D.Wang and J.Huang,“Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form,”IEEE Transactions on Neural Networks,vol.16,no.1,pp.195–202,2005.
[16]S.Gao,H.Dong,B.Ning,and X.Sun,“Neural adaptive control for uncertain mimo systems with constrained input via intercepted adaptation and single learning parameter approach,”Nonlinear Dynamics,vol.82,no.3,pp.1109–1126,2015.
[17]S.Gao,H.Dong,S.Lyu,and B.Ning,“Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation,”International Journal of Control,vol.17,no.8,pp.1447–1466,2016.
[18]S.Gao,H.Dong,B.Ning,and L.Chen,“Neural adaptive control for uncertain nonlinear system with input saturation:State transformation based output feedback,”Neurocomputing,vol.159,pp.117–125,2015.
[19]S.Gao,B.Ning,and H.Dong,“Fuzzy dynamic surface control for uncertain nonlinear systems under input saturation via truncated adaptation approach,”Fuzzy Sets&Systems,vol.290,pp.100–117,2016.
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