Neural-network-based cooperative adaptive identification of nonlinear systems

This paper considers the problem of cooperative adaptive identification for a class of nonlinear systems via neural networks. The proposed adaptive laws of neural network weights are distributed, and the interconnection topologies are established among identification models in order to share their data on-line. It is proved that if the interconnection topologies are undirected and connected, then all adaptive laws of neural network weights for the same system function can converge to a small neighborhood around their optimal values over a union of sets consisting of system trajectories. Thus, the learned system model has the better generalization capability. A simulation example are provided to verify the effectiveness and advantages of the algorithms proposed in this paper.

[1]  Marios M. Polycarpou,et al.  Stable adaptive neural control scheme for nonlinear systems , 1996, IEEE Trans. Autom. Control..

[2]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[3]  Shuzhi Sam Ge,et al.  Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach , 2008, IEEE Transactions on Neural Networks.

[4]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[5]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[6]  Zhijun Li,et al.  Adaptive Fuzzy Control for Synchronization of Nonlinear Teleoperators With Stochastic Time-Varying Communication Delays , 2011, IEEE Transactions on Fuzzy Systems.

[7]  Cong Wang,et al.  Deterministic learning of nonlinear dynamical systems , 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.

[8]  Peng Shi,et al.  Robust Output Feedback Tracking Control for Time-Delay Nonlinear Systems Using Neural Network , 2007, IEEE Transactions on Neural Networks.

[9]  Francis J. Doyle,et al.  Identification and Control Using Volterra Models , 2001 .

[10]  S. Ge,et al.  Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[11]  Shuzhi Sam Ge,et al.  Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities , 2010, IEEE Transactions on Neural Networks.

[12]  David J. Hill,et al.  Deterministic Learning Theory , 2009 .

[13]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[14]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[15]  Dan Wang,et al.  Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form , 2005, IEEE Transactions on Neural Networks.

[16]  Sarangapani Jagannathan,et al.  Control of a class of nonlinear discrete-time systems using multilayer neural networks , 2001, IEEE Trans. Neural Networks.

[17]  Zhijun Li,et al.  Adaptive Motion/Force Control of Mobile Under-Actuated Manipulators With Dynamics Uncertainties by Dynamic Coupling and Output Feedback , 2010, IEEE Transactions on Control Systems Technology.

[18]  Shaocheng Tong,et al.  Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems , 2011, IEEE Transactions on Neural Networks.

[19]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[20]  Shuzhi Sam Ge,et al.  Adaptive neural network control for strict-feedback nonlinear systems using backstepping design , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[21]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.