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This paper investigates global stabilization via adaptive event-triggered output feedback for a class of uncertain nonlinear systems. Typically, unknown polynomial-function rate is admitted in the unmeasurable-state dependent growth of the systems. This calls for an advanced compensation strategy based on dynamic high gain, which in turn requires more intelligent execution in the event-triggered control architecture. To this end, a novel event-triggering mechanism is designed with two events separately evaluating the behaviors of dynamic gain and the controller signal. Particularly, the event on controller signal is enforced to suspend for a certain time after each execution to guarantee a positive lower bound for the inter-execution intervals. More importantly, the suspension time and the threshold therein are both online adjusted according to dynamic gain (rather than pre-specified), which could become small enough as the dynamic gain increases. This ensures timely execution for the effectiveness of adaptive compensation. Then, with the dynamic gain delicately designed to counteract the influence of the execution error, an event-triggered controller via adaptive output feedback is proposed to make the original system states and observer states converge to zero. Further attempt is performed for more efficient resource saving and disturbance tolerance. [ABSTRACT FROM AUTHOR]

This article investigates global practical output tracking via adaptive output feedback for a class of uncertain nonlinear systems. Remarkably, the control problem under investigation is in the context of unknown control directions and output feedback, which severely restricts the system nonlinearities. Nevertheless, the growth on the unknown system nonlinearities in this article is extended from polynomial (of output) to arbitrary function, and meanwhile no further a priori information is required on the reference signal to be tracked. Specifically, sufficiently smooth pseudosign and pseudo-dead-zone functions are introduced acting as substitutions of sign and dead-zone functions in the literature, respectively. Typically, based on the pseudo-dead-zone function, a delicate estimate on the unknown system nonlinearities is established and a novel Lyapunov function is proposed. This, together with the elegant system performance analysis, is quite critical to remove the polynomial constraint and overcome the technical obstacle stemming from the essential extension. Additionally owing to the two refined functions, the use of smooth domination/treatment is moderately avoided in control design that enables the established control strategy to be tighter and less conservative. It turns out that under the proposed adaptive output-feedback controller, all the closed-loop system states are globally bounded, while the system tracking error enters a prescribed λ neighborhood of the origin in finite time and remains inside thereafter. A simulation example illustrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses the event-triggered tracking for a class of uncertain nonlinear systems. Essentially different from the related works, the nonlinear systems allow serious (parametric) uncertainties, while no further a priori information, excepting some coarse information, is required on the reference signal to be tracked. This makes event-triggered control more challenging, partially because the sampling error cannot be precisely bounded any more. As main contributions of this paper, new adaptive event-triggered tracking schemes are proposed for the systems in two event-triggering architectures with different roles of the event-triggering mechanism on the information transmitting and control computing/updating. Much importantly, a dynamic gain is incorporated in either scheme not only to counteract the serious uncertainties, but also to overcome the bad influence of the sampling error. Based on the dynamic gain, two adaptive event-triggered controllers are designed with distinct event-triggering mechanisms, respectively. Particularly, to ensure application flexibility, one of the triggering mechanisms is rendered relatively independent of the controller signal. The designed event-triggered controllers are shown to achieve the practical tracking for the systems, that is, the prespecified arbitrary tracking accuracy can be guaranteed (a comparable objective to that via continuous-time control), while avoiding infinitely fast sampling/execution. [ABSTRACT FROM AUTHOR]

This paper considers the control design with prescribed performance for uncertain nonlinear systems. Different from the existing literature, the systems under investigation possess unknown control directions and nonparametric uncertainties. Even so, the control objective to be achieved is still refined, not only to ensure the basic performance on stabilization, but also to guarantee certain prescribed performance required in real applications. Motivated by the funnel control method, a time-varying stabilizing scheme is developed for the systems, to effectively handle the serious uncertainties and to successfully guarantee the states to converge to zero with a prescribed rate. [ABSTRACT FROM AUTHOR]

This paper is devoted to the analysis methods/tools to stochastic convergence and stochastic adaptive output-feedback control. As the first contribution, a general stochastic convergence theorem is proposed for stochastic nonlinear systems. The theorem doesn't necessarily involve a positive-definite function of the system states with negative-semidefinite infinitesimal, essentially different from stochastic LaSalle's theorem (see e.g., [1]), and hence can provide more opportunities to achieve stochastic convergence. Moreover, as a direct extension of the convergence theorem, a general version of stochastic Barb ă lat's lemma is obtained, which requires the concerned stochastic process to be almost surely integrable, rather than absolutely integrable in the sense of expectation, unlike in [2]. As the second contribution, supported by the general stochastic convergence theorem, an adaptive output-feedback control strategy is established for the global stabilization of a class of stochastic nonlinear systems with severe parametric uncertainties coupled to unmeasurable states. Its feasibility analysis takes substantial effort, and is largely based on the general stochastic convergence theorem. Particularly, for the resulting closed-loop system, certain stochastic boundedness and integrability are shown by the celebrated nonnegative semimartingale convergence theorem, and furthermore, the desired stochastic convergence is achieved via the general stochastic convergence theorem. [ABSTRACT FROM PUBLISHER]

In this paper, the global adaptive regulation is considered for a class of uncertain stochastic high-order nonlinear systems with serious nonlinearities and unknowns. For details, the considered systems admit the general forms which include the strict-feedback systems as the special case, and allow the more complicated nonlinearities. Moreover, the system model suffers from both the unknown parameterized nonlinear growth conditions and the unknown time-varying control gain with unknown control direction (i.e. the sign of the control gain). By introducing two new design parameters and skillfully combining the adaptive design methods with the Nussbaum-type function based technique, a desired adaptive regulation control scheme is successfully presented, by which the obtained smooth controller can effectively guarantee that all the closed-loop system states are bounded almost surely, and especially the original system states converge to the origin with probability one. Finally, to demonstrate the feasibility of the control scheme, a numerical example is given. [ABSTRACT FROM AUTHOR]