MTN Optimal Control of SISO Nonlinear Time-varying Discrete-time Systems for Tracking by Output Feedback

[1]  Didier Maquin,et al.  State and output feedback control for Takagi-Sugeno systems with saturated actuators , 2016 .

[2]  Ahmad B. Rad,et al.  Self-tuning PID controller using Newton-Raphson search method , 1997, IEEE Trans. Ind. Electron..

[3]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[4]  Ali A. Minai,et al.  Back-propagation heuristics: a study of the extended delta-bar-delta algorithm , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[5]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[6]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[7]  Hamid Reza Karimi,et al.  Robust H∞ control of uncertain stochastic Markovian jump systems with mixed time-varying delays , 2017, Int. J. Syst. Sci..

[8]  Juan Li,et al.  Control design for arbitrary complex nonlinear discrete-time systems based on direct NNMRAC strategy , 2011 .

[9]  Derong Liu,et al.  Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning , 2014, Neural Networks.

[10]  Chih-Min Lin,et al.  Recurrent Fuzzy Neural Network Control for Mimo Nonlinear Systems , 2008, Intell. Autom. Soft Comput..

[11]  B Zhou.  A Dynamics Model Based on Intermittent Feedback Multi-dimensional Taylor Network , 2014 .

[12]  Wei Li,et al.  Backstepping control for periodically time-varying systems using high-order neural network and Fourier series expansion. , 2010, ISA transactions.

[13]  Yi Lin,et al.  A Novel Modeling Method Based on Multi-Dimensional Taylor Network and its Application in Time Series Prediction , 2014 .

[14]  Young Hoon Joo,et al.  Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays , 2017, Neural Computing and Applications.

[15]  A. Patrikar,et al.  Nonlinear system identification and adaptive control using polynomial networks , 1996 .

[16]  Dimitris C. Theodoridis,et al.  Direct adaptive neuro‐fuzzy trajectory tracking of uncertain nonlinear systems , 2012 .

[17]  Dale E. Seborg,et al.  A SELF-TUNING CONTROLLER WITH A PID STRUCTURE , 1983 .

[18]  Nikolaos I. Margaris,et al.  Explicit Analytical PID Tuning Rules for the Design of Type-III Control Loops , 2013, IEEE Transactions on Industrial Electronics.

[19]  Bo Zhou,et al.  Time series forecasting based on the empirical mode decomposition multi-dimensional Taylor network model , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[20]  Hong-Sen Yan,et al.  Optimal Adjustment Control of SISO Nonlinear Systems Based on Multi-Dimensional Taylor Network only by Output Feedback , 2014 .

[21]  Charles L. Phillips,et al.  Feedback Control Systems , 1988 .

[22]  Tianyou Chai,et al.  Robust self‐tuning PI decoupling control of uncertain multivariable systems , 2012 .

[23]  Youguo Pi,et al.  PID neural networks for time-delay systems , 2000 .

[24]  Khoshnam Shojaei,et al.  Neural adaptive robust output feedback control of wheeled mobile robots with saturating actuators , 2015 .

[25]  B. Nagaraj,et al.  Tuning of a PID Controller Using Soft Computing Methodologies Applied to Basis Weight Control in Paper Machine , 2011 .

[26]  Aydogan Savran,et al.  Multifeedback-Layer Neural Network , 2007, IEEE Transactions on Neural Networks.

[27]  Petr Husek,et al.  Decentralized PI Controller Design Based on Phase Margin Specifications , 2014, IEEE Transactions on Control Systems Technology.

[28]  Zhao Lu,et al.  Identification and Control Of Chaotic Systems Via Recurrent High-Order Neural Networks , 2007, Intell. Autom. Soft Comput..

[29]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[30]  Liang Jin,et al.  Intelligent Control for Nonlinear Systems using Dynamic Neural Networks with Robotic Applications , 1995, Intell. Autom. Soft Comput..

[31]  James R. Leigh.  Applied Digital Control , 1985 .

[32]  Wei Sun,et al.  Neural Network-Based Robust Tracking Control For Robots , 2009, Intell. Autom. Soft Comput..

[33]  Yahya H. Zweiri,et al.  A three-term backpropagation algorithm , 2003, Neurocomputing.

[34]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[35]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[36]  Hamid Reza Karimi,et al.  Reliable Output Feedback Control of Discrete-Time Fuzzy Affine Systems With Actuator Faults , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[37]  Wen Yu,et al.  Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm , 2007, Neurocomputing.

[38]  M. Syed Ali,et al.  Robust H∞ control for uncertain Markovian jump systems with mixed delays , 2016 .

[39]  Hong-Sen Yan,et al.  Asymptotic tracking and dynamic regulation of SISO non-linear system based on discrete multi-dimensional Taylor network , 2017 .

[40]  Jianhua Zhang,et al.  A novel PSO algorithm based on an incremental-PID-controlled search strategy , 2016, Soft Comput..

[41]  Hamid Reza Karimi,et al.  New results on H∞ dynamic output feedback control for Markovian jump systems with time‐varying delay and defective mode information , 2014 .

[42]  C. Tseng.  Robust tracking control design for uncertain robotic systems with persistent bounded disturbances , 2008 .

[43]  B. Nagaraj,et al.  Tuning of a PID Controller using Soft Computing Methodologies Applied to Moisture Control in Paper Machine , 2012, Intell. Autom. Soft Comput..

[44]  M. Syed Ali,et al.  Novel delay-dependent robust H∞ control of uncertain systems with distributed time-varying delays , 2014, Appl. Math. Comput..

[45]  Liu Lu,et al.  MOSキャパシタのC‐V曲線の周波数分散に及ぼす酸化物トラップの等価分布容量モデル【Powered by NICT】 , 2016 .

[46]  Shu Huailin.  ANALYSIS OF PID NEURAL NETWORK MULTIVARIABLE CONTROL SYSTEMS , 1999 .

[47]  Kaspar Althoefer,et al.  Stability analysis of a three-term backpropagation algorithm , 2005, Neural Networks.

[48]  Jianbin Qiu,et al.  Approaches to T–S Fuzzy-Affine-Model-Based Reliable Output Feedback Control for Nonlinear Itô Stochastic Systems , 2017, IEEE Transactions on Fuzzy Systems.

[49]  Barak A. Pearlmutter.  Gradient Descent: Second Order Momentum and Saturating Error , 1991, NIPS.

[50]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[51]  Tore Hägglund,et al.  The future of PID control , 2000 .

[52]  Sarangapani Jagannathan,et al.  Adaptive neural network‐based optimal control of nonlinear continuous‐time systems in strict‐feedback form , 2014 .

[53]  Bor-Sen Chen,et al.  Multiobjective PID control design in uncertain robotic systems using neural network elimination scheme , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[54]  Hongye Su,et al.  Modular‐based adaptive control of uncertain nonlinear time‐varying systems with piecewise jumping parameters , 2014 .

[55]  P. S. Sastry,et al.  Analysis of the back-propagation algorithm with momentum , 1994, IEEE Trans. Neural Networks.

[56]  Hassan K. Khalil,et al.  Output feedback control of nonlinear systems using RBF neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[57]  Rui Yan,et al.  Iterative learning control design without a priori knowledge of the control direction , 2004, Autom..

[58]  Yi Lin,et al.  A Novel Nonlinear Time Series Prediction Method and its Application in Structural Vibration Response Prediction , 2014 .