Adaptive fuzzy backstepping dynamic surface control for output-constrained non-smooth nonlinear dynamic system

Output-constrained backstepping dynamic surface control (DSC) is proposed for the purpose of output constraint and precise output positioning of a strict feedback single-input, single-output dynamic system in the presence of deadzone and uncertainty. A symmetric barrier Lyapunov function (BLF) is employed to meet the output constraint requirement using DSC as an alternative method of backstepping control that is adopted mainly to deal with the BLF’s constraint control. However, using the ordinary DSC method with the BLF limits the selection of the control gain whereas this limitation does not exist in the backstepping structure. To remove this limitation, we propose a partial backstepping DSC method in which backstepping control is added only in the first recursive DSC design step. For precise positioning, an inverse deadzone method and adaptive fuzzy system are introduced to handle unknown deadzone and unmodeled nonlinear functions. We show that the semiglobal boundedness of the overall closed-loop signals is guaranteed, the tracking error converges within the prescribed region, and precise positioning performance is ensured. The proposed control scheme is experimentally evaluated using a robot manipulator.

[1]  S. Tong,et al.  Observer-based adaptive fuzzy backstepping dynamic surface control for a class of non-linear systems with unknown time delays , 2011 .

[2]  Jin Bae Park,et al.  Adaptive Dynamic Surface Control of Flexible-Joint Robots Using Self-Recurrent Wavelet Neural Networks , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Petros A. Ioannou,et al.  Backstepping control of linear time-varying systems with known and unknown parameters , 2003, IEEE Trans. Autom. Control..

[4]  Francis Eng Hock Tay,et al.  Barrier Lyapunov Functions for the control of output-constrained nonlinear systems , 2009, Autom..

[5]  Jang-Myung Lee,et al.  Friction and uncertainty compensation of robot manipulator using optimal recurrent cerebellar model articulation controller and elasto-plastic friction observer , 2011 .

[6]  Weisheng Chen Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks , 2009 .

[7]  Swaroop Darbha,et al.  Dynamic surface control for a class of nonlinear systems , 2000, IEEE Trans. Autom. Control..

[8]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[9]  Euntai Kim,et al.  Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic , 2004, IEEE Trans. Fuzzy Syst..

[10]  Yeong-Chan Chang,et al.  Robust tracking control for nonlinear MIMO systems via fuzzy approaches , 2000, Autom..

[11]  Shuzhi Sam Ge,et al.  Control of Coupled Vessel, Crane, Cable, and Payload Dynamics for Subsea Installation Operations , 2011, IEEE Transactions on Control Systems Technology.

[12]  J. P. Hwang,et al.  Robust tracking control of an electrically driven robot: adaptive fuzzy logic approach , 2006, IEEE Transactions on Fuzzy Systems.

[13]  Jin Bae Park,et al.  Adaptive dynamic surface control for disturbance attenuation of nonlinear systems , 2009 .

[14]  Marios M. Polycarpou,et al.  A Robust Adaptive Nonlinear Control Design , 1993, 1993 American Control Conference.

[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]  Keng Peng Tee,et al.  Adaptive Control of Electrostatic Microactuators With Bidirectional Drive , 2009, IEEE Transactions on Control Systems Technology.

[17]  Gang Tao,et al.  Adaptive control of plants with unknown dead-zones , 1994 .

[18]  Keng Peng Tee,et al.  Control of nonlinear systems with partial state constraints using a barrier Lyapunov function , 2011, Int. J. Control.

[19]  Keng Peng Tee,et al.  Control of nonlinear systems with time-varying output constraints , 2009, 2009 IEEE International Conference on Control and Automation.

[20]  Keng Peng Tee,et al.  Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function , 2010, IEEE Transactions on Neural Networks.

[21]  R. Mahony,et al.  Integrator Backstepping using Barrier Functions for Systems with Multiple State Constraints , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[22]  P. P. Yip,et al.  Multiple Sliding Surface Control: Theory and Application , 2000 .

[23]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[24]  Yan Lin,et al.  A robust adaptive dynamic surface control for a class of nonlinear systems with unknown Prandtl–Ishilinskii hysteresis , 2011 .

[25]  Jun Zhao,et al.  Tracking control for output-constrained nonlinear switched systems with a barrier Lyapunov function , 2013, Int. J. Syst. Sci..