Event-triggered adaptive neural finite-time tracking control for non-strict feedback stochastic nonlinear systems with unknown time-varying control directions and input nonlinearities
JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL(2023)
摘要
This paper focuses on dynamic-surface-based event-triggered adaptive neural finite-time tracking control for a category of non-strict feedback stochastic nonlinear system subject to unknown time-varying control directions, and input nonlinearities. Firstly, an improved event-triggered mechanism is presented, which simultaneously considers tracking error variables and compensating for event errors in threshold value. Secondly, a novel Lemma is provided to address concurrently the issues of Nussbaum terms in stability analysis and stochastic system finite-time stability. Thirdly, neural networks are utilized to cope with unknown functions. In addition, the Nussbaum functions are applied to identify time-varying unknown control directions and input nonlinearities. Moreover, the errors of the dynamic surface technique are compensated successfully by error compensation signals. By combining the proposed Lemma 1 and event-triggered adaptive neural finite-time control scheme, finite-time stability of considered systems in probability can be guaranteed, the tracking error can drive to a small neighborhood of the origin in finite time, and the Zeno behavior can be excluded. Finally, the effectiveness of the designed control scheme has been demonstrated through a second-order numerical system and a mass-spring-damper example.
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关键词
Event-triggered control,finite-time control,stochastic nonlinear systems,adaptive neural control,unknown time-varying control directions,input nonlinearities
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