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Jiang, Xiao-Yue; Li, Yuan-Xin

IBLF-based event-triggered adaptive learning control of nonlinear systems with full state constraints. (English) Zbl 07842319

Int. J. Adapt. Control Signal Process. 36, No. 8, 1816-1834 (2022).
Summary: This article focuses on the adaptive asymptotic learning tracking control problem of nonlinear systems with full state constraints. First, an adaptive neural network tracking algorithm is proposed which combines a time-varying feedback element and robust compensation feedback element in the form of smooth function to guarantee that output signal tracks the reference signal asymptotically. Apart from this, a time-varying integral barrier Lyapunov function is utilized to ensure that the system states are always kept in the constraint region. Furthermore, the event-triggered mechanism is designed to efficiently reduce unnecessary transmissions. Compared with the other literatures, the original state constraint problem is directly solved by the proposed adaptive control algorithm. Finally, simulation is included to validate the built theoretical results.
{© 2022 John Wiley & Sons Ltd.}

Cited in 1 Document

MSC:

93C40 Adaptive control/observation systems
93C65 Discrete event control/observation systems
93D21 Adaptive or robust stabilization

Keywords:

backstepping; event-triggered control; full state constraints; nonlinear uncertain systems
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References:

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