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We propose a new event-generating rule for event-triggered control of nonlinear systems with external disturbances. We consider control systems that are input-to-state stabilizable with the external disturbance and the sampling errors as the inputs. By introducing a decision-making process in the event trigger, our sampling intervals are guaranteed to be larger than a positive lower bound. Moreover,...
This paper studies an event-triggered control problem for nonlinear systems in the presence of external disturbances. To avoid infinitely fast sampling caused by disturbances, a new event-triggering mechanism is proposed, which depends not only on the system state but also on an estimation of the influence of the external disturbance. Moreover, the closed-loop event-triggered system is proved to be...
This paper studies the quantized partial-state feedback stabilization of a class of nonlinear cascaded systems with dynamic uncertainties. Under the assumption that the dynamic uncertainties are input-to-state practically stable, we develop a novel recursive design method for quantized stabilization by taking into account the influence of quantization and using the small-gain theorem. When the dynamic...
Recently, a deterministic learning theory was proposed for identification and rapid pattern recognition of uncertain nonlinear dynamical systems. In this paper, we investigate deterministic learning of discrete-time nonlinear systems. For periodic or recurrent dynamical patterns, the persistent excitation (PE) condition can be satisfied by a regression subvector constructed from the neurons near the...
In this paper, we investigate deterministic learning from adaptive neural control of general Brunovsky systems, in which the affine terms are unknown functions of system states. We firstly present an extension of a recent result on stability analysis of linear time varying (LTV) systems. We then analyze the difficulties caused by the unknown affine term in deterministic learning for general Brunovsky...
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