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Output Feedback Adaptive Control for Stochastic Non-strict-feedback System with Dead-zone

Yumei Sun*, Bingwei Mao, Hongxia Liu, and Shaowei Zhou
International Journal of Control, Automation, and Systems, vol. 18, no. 10, pp.2621-2629, 2020

Abstract : This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear stochastic non-strict feedback system with dead-zone input. A novel adaptive NN output feedback control approach is first proposed for stochastic non-strict feedback nonlinear systems. In order to solve the problem of dead-zone input, a linear decomposition method is proposed. On the basis of the state observer, an output feedback adaptive NN controller is designed by a backstepping approach. It is shown that the proposed controller guarantees that all the signals of the closed-loop systems are semi-globally uniformly bounded in probability. Simulation results further illustrate the effectiveness of the proposed approach.

Download: http://link.springer.com/article/10.1007/s12555-019-0876-9

Keyword : Adaptive neural control, backstepping, dead-zone, state observer, stochastic non-strict feedback nonlinear systems

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