Adaptive neural/fuzzy control for interpolated nonlinear systems

Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. We propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Both indirect and direct adaptive control methods are developed, where the spatially localized models (in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks) are used as online approximators to learn the unknown dynamics of the system. Without assumptions on rate of change of system dynamics, the proposed adaptive control methods guarantee that all internal signals of the system are bounded and the tracking error is asymptotically stable. The performance of the adaptive controller is demonstrated using a jet engine control problem.

[1]  Kazuo Tanaka,et al.  Parallel distributed compensation of nonlinear systems by Takagi-Sugeno fuzzy model , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[2]  Y. Stepanenko,et al.  Adaptive control of a class of nonlinear systems with fuzzy logic , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[3]  Chen-Chung Liu,et al.  Adaptively controlling nonlinear continuous-time systems using multilayer neural networks , 1994, IEEE Trans. Autom. Control..

[4]  Tian-You Chai,et al.  A new robust adaptive control algorithm for linear time-varying plants , 1998, Int. J. Syst. Sci..

[5]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[6]  Kevin M. Passino,et al.  Stable adaptive control using fuzzy systems and neural networks , 1996, IEEE Trans. Fuzzy Syst..

[7]  C. Kravaris,et al.  Controller synthesis for time-varying systems by input/output linearization , 1997 .

[8]  A. Isidori,et al.  Adaptive control of linearizable systems , 1989 .

[9]  Wilson J. Rugh,et al.  Gain scheduling dynamic linear controllers for a nonlinear plant , 1995, Autom..

[10]  Shrider Adibhatla,et al.  MODEL-BASED INTELLIGENT DIGITAL ENGINE CONTROL (MoBIDEC) , 1997 .

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

[12]  Yixin Diao,et al.  Fault diagnosis for a turbine engine , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[13]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[14]  Kevin M. Passino,et al.  Adaptive control for a class of nonlinear systems with a time-varying structure , 2001, IEEE Trans. Autom. Control..

[15]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[16]  Wei Wu,et al.  Adaptive feedforward and feedback control of non-linear time-varying uncertain systems , 1999 .

[17]  J. Watkins,et al.  Adaptive control of time-varying systems based on parameter set estimation , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[18]  Yixin Diao,et al.  Stable adaptive control of feedback linearizable time-varying non-linear systems with application to fault-tolerant engine control , 2004 .

[19]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[20]  Petros A. Ioannou,et al.  Linear Time-Varying Systems: Control and Adaptation , 1992 .

[21]  Manolis A. Christodoulou,et al.  Adaptive control of unknown plants using dynamical neural networks , 1994, IEEE Trans. Syst. Man Cybern..

[22]  Li-Xin Wang,et al.  A supervisory controller for fuzzy control systems that guarantees stability , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[23]  Kazuo Tanaka,et al.  Stability analysis and design of fuzzy control systems , 1992 .

[24]  George A. Rovithakis,et al.  Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks , 1995, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  Yixin Diao,et al.  Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine , 2001, IEEE Trans. Control. Syst. Technol..

[26]  Kevin M. Passino,et al.  Indirect adaptive control for a class of non-linear systems with a time-varying structure , 2001 .

[27]  Wei Lin,et al.  Global Robust Stabilization of Minimum-Phase Nonlinear Systems with Uncertainty , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[28]  Shuzhi Sam Ge.  Book reviews: Stable adaptive control and estimation for nonlinear systems-neural and fuzzy approximator techniques , 2003 .

[29]  Wilson J. Rugh,et al.  Analytical Framework for Gain Scheduling , 1990, 1990 American Control Conference.

[30]  M. Polycarpou,et al.  Stable adaptive tracking of uncertain systems using nonlinearly parametrized on-line approximators , 1998 .

[31]  R. Marino,et al.  Robust adaptive state-feedback tracking for nonlinear systems , 1998, IEEE Trans. Autom. Control..

[32]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[33]  Sheng-De Wang,et al.  A self-organizing adaptive fuzzy controller , 1996, Fuzzy Sets Syst..