The problem of robust output tracking is studied for a class of uncertain nonlinear systems in the presence of structure uncertainties, external disturbances, and unknown time-varying virtual control coefficients. In this study, it is supposed that the upper bounds of external disturbances and that the upper and lower bounds of unknown time-varying virtual control coefficients are unknown. By employing a simple structure neural network (NN), the unknown structure uncertainties are approximated. A class of backstepping approach-based adaptive robust controllers is synthesized for such uncertain nonlinear systems. By making use of Lyapunov functional approach, it is also shown that the proposed adaptive robust backstepping output tracking controller can guarantee the tracking error between the system output and the desired reference signal to converge asymptotically to zero. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed controller.
Adaptive Robust Backstepping Output Tracking Control for a Class of Uncertain Nonlinear Systems Using Neural Network
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 29, 2016; final manuscript received November 30, 2017; published online March 7, 2018. Assoc. Editor: Srinivasa M. Salapaka.
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Wang, Y., Xu, L., and Wu, H. (March 7, 2018). "Adaptive Robust Backstepping Output Tracking Control for a Class of Uncertain Nonlinear Systems Using Neural Network." ASME. J. Dyn. Sys., Meas., Control. July 2018; 140(7): 071014. https://doi.org/10.1115/1.4039151
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