SummaryIn this paper, adaptive output feedback tracking control is developed for a class of stochastic nonlinear systems with dynamic uncertainties and unmeasured states. Neural networks are used to approximate the unknown nonlinear functions. K‐filters are designed to estimate the unmeasured states. An available dynamic signal is introduced to dominate the unmodeled dynamics. By combining dynamic surface control technique with backstepping, the condition in which the approximation error is assumed to be bounded is avoided. Using It ô formula and Chebyshev's inequality, it is shown that all signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to illustrate the effectiveness of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.

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In this paper, the problem of neural adaptive dynamic surface quantized control is studied the first time for a class of pure-feedback nonlinear systems in the presence of state and output constraint and unmodeled dynamics. The considered system is under the control of a hysteretic quantized input signal. Two types of one-to-one nonlinear mapping are adopted to transform the pure-feedback system with different output and state constraints into an equivalent unconstrained pure-feedback system. By designing a novel control law based on modified dynamic surface control technique, many assumptions of the quantized system in early literary works are removed. The unmodeled dynamics is estimated by a dynamic signal and approximated based on neural networks. The stability analysis indicates that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the output and all the states remain in the prescribed time-varying or constant constraints. Two numerical examples with a coarse quantizer show that the proposed approach is effective for the considered system. KEYWORDSadaptive quantized control, output and state constraints, pure-feedback systems, robust adaptive DSC, unmodeled dynamics Int J Robust Nonlinear Control. 2018;28:3357-3375. wileyonlinelibrary.com/journal/rnc

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