Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 26, Issue 7, Pages 1532-1538Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2014.2344019
Keywords
Adaptive control; asymmetric saturation; backstepping; dynamic surface control (DSC); neural networks (NNs)
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Funding
- National Natural Science Foundation of China [61203095]
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In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided using dynamic surface control. Using radial basis function NN, adaptive control is developed to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design constants. The effectiveness of the proposed control is demonstrated in the simulation study.
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