A dynamic neural network (DNN) observer-based output feedback controller for uncertain nonlinear systems with bounded disturbances is developed. The DNN-based observer works in conjunction with a dynamic filter for state estimation using only output measurements during online operation. A sliding mode term is included in the DNN structure to robustly account for exogenous disturbances and reconstruction errors. Weight update laws for the DNN, based on estimation errors, tracking errors, and the filter output are developed, which guarantee asymptotic regulation of the state estimation error. A combination of a DNN feedforward term, along with the estimated state feedback and sliding mode terms yield an asymptotic tracking result. The developed output feedback (OFB) method yields asymptotic tracking and asymptotic estimation of unmeasurable states for a class of uncertain nonlinear systems with bounded disturbances. A two-link robot manipulator is used to investigate the performance of the proposed control approach.
Dynamic Neural Network-Based Output Feedback Tracking Control for Uncertain Nonlinear Systems
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 3, 2016; final manuscript received January 20, 2017; published online May 10, 2017. Assoc. Editor: Yongchun Fang.
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Dinh, H. T., Bhasin, S., Kamalapurkar, R., and Dixon, W. E. (May 10, 2017). "Dynamic Neural Network-Based Output Feedback Tracking Control for Uncertain Nonlinear Systems." ASME. J. Dyn. Sys., Meas., Control. July 2017; 139(7): 074502. https://doi.org/10.1115/1.4035871
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