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Communication network could suffer uncertainties originating from link faults and network attacks. This situation, in multi-agent systems, can be featured by unknown weights of network topology. To impair the influence of the uncertain topology, typical compensation should be taken into account in distributed protocols to the workability of multi-agent systems. This paper, in the context of uncertain topology, addresses finite-time leader-following consensus for second-order uncertain nonlinear multi-agent systems. In addition to uncertainties in topology, the systems also allow unknown control coefficients, which together with the unknown weights renders the realisation of finite-time leader-following consensus nontrivial. Specifically, a fully distributed protocol based on distributed finite-time observer is designed via integrating the adaptive compensation scheme. Notably, in the designed protocol, dynamic high gains are introduced for the compensations of the network uncertainties and agents uncertainties. It turns out that the designed fully distributed protocol guarantees the global finite-time leader-following consensus. Simulation examples are provided to illustrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

This article is devoted to explore the periodic event-triggered stabilization for continuous-time stochastic systems, and to develop analysis tools/methods for stochastic periodic event-triggered control. Notably, without real-time monitoring of system behavior as in the scenario with continuous event evaluation, it is critical to delicately estimate and govern the execution/sampling error to achieve the desired system performance. This, under stochastic effects, would be substantially challenging, since the behavior of the stochastic system is hard to predict and is disparate between trials even with the same initial conditions. In this article, a framework of global stabilization via periodic event-triggered output-feedback is established: 1) a criterion condition based on the ISS-Lyapunov function is presented for the feasibility of the desired event-triggered stabilization; 2) both the asymptotic stabilization and exponential stabilization are achieved for the systems, with delicately specifying the periodic event-triggering mechanism; and 3) the involved analysis, without applying the well-known Lyapunov theorems, can serve as a pattern from estimating sampling and execution errors to assess the closed-loop stability for stochastic periodic event-triggered control. Moreover, based on the established framework, we contribute the stabilizing controller design via periodic event-triggered output-feedback for a class of stochastic nonlinear systems. [ABSTRACT FROM AUTHOR]

LaSalle theorems, sometimes called as invariance-like theorems, have witnessed abundant applications as powerful tools of analysing system convergence. However, these theorems have twofold limitations: On the one hand, the uniformity of convergence with respect to initial state values cannot be offered, which indicates that the possibility of excessively slow convergence could not be ruled out for a given bounded set of initial state values. On the other hand, unbounded time-variations are not allowed in the systems, and meanwhile, the boundedness of all the states are required even if one merely concerns the convergence of partial states, precluding many scenarios with the unboundedness (e.g., induced by stochastic noise) for some states defined on the whole time horizon. Towards the limitations of LaSalle theorems, this paper seeks to further develop convergence theorems in the stochastic framework. First, a Lyapunov-like function based theorem on the convergence owning the uniformity with respect to initial state values is presented for Itô-type stochastic systems, with the infinitesimal of Lyapunov-like functions exploited more delicately than those in stochastic LaSalle theorems. Based on this, a framework of adaptive stabilization with the uniformity of convergence is established for uncertain stochastic nonlinear systems. In particular, the performance specifications involved are impossible to achieve by applying LaSalle theorems as in the related results. Second, an enlarged convergence theorem is established with Lyapunov-like conditions moderately relaxed, which allows the unboundedness for partial states defined on the whole time horizon and has potential applications in the presence of unbounded time-variations. What is notable, the enlarged convergence theorem is nontrivial to verify, which compels us to propose an extended Barbălat lemma with the conventional requirement of uniform continuity relaxed in the stochastic framework. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the global practical tracking via adaptive output‐feedback for a class of uncertain nonlinear systems. Essentially different from the closely related literature, the system under investigation possesses unknown time‐varying control coefficients and a polynomial‐of‐output growth rate, and meanwhile, the system nonlinearities and the reference signal allow serious unknowns. For this, an adaptive observer is designed to reconstruct the system unmeasured states, where a new dynamic gain is introduced to compensate the serious unknowns in the system nonlinearities and the reference signal. Based on this and by backstepping technique, an adaptive output‐feedback controller is successfully designed, such that all the states of the closed‐loop system are bounded, and the tracking error will be prescribed sufficiently small after a finite time. A numerical simulation is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To date, most schemes of adaptive stabilization are confined to convergence, and cannot specify the convergence rate. As such, the possibility of excessively slow convergence could not be ruled out, which undermines the application scope of adaptive schemes. Such imperfection is mainly due to the limited capability of compensation/learning mechanisms which lack the ingredients affording specific convergence rate. For stronger applicability, more comprehensive compensation/learning mechanisms need to be exploited, inevitably aggravating control synthesis and analysis. This paper aims to enhance adaptive control in terms of convergence rate, and particularly, seeks exponential regulation via adaptive output feedback for stochastic nonlinear systems allowing large uncertainties coupling to unmeasurable states and dynamic uncertainties. To this end, a refined adaptive output-feedback scheme is established in the stochastic framework, though no non-stochastic counterpart exists. Critically, a novel dynamic high gain is introduced in a universal manner, with its updating law delicately incorporating the gain-dependent time-varying information in exponential form. The dynamic gain would not only become sufficiently large to capture the large uncertainties, but also render the desired convergence with the rate online regulated to match the dynamic uncertainties. In this way, an adaptive output-feedback controller based on dynamic-gain observer is designed, which guarantees the system and observer states to almost surely converge to zero at an exponential rate. The stochastic framework complicates the validity verification of the established scheme, entailing subtle martingale-based analysis. Correspondingly, a distinctive analysis pattern is developed for the resulting closed-loop system, to attain expected stochastic convergence and boundedness. [ABSTRACT FROM AUTHOR]

This paper is concerned with the global practical tracking via adaptive output feedback for a class of uncertain nonlinear systems. The system under investigation possesses function control coefficients, the polynomial-of-output growth rate and serious unknowns in the system nonlinearities and the reference signal, and hence is essentially different from those in the closely related literature. To solve the problem, a high-gain observer is introduced to reconstruct the unmeasured system states. The involved high gain is the multiplication of two dynamic gains: one is to compensate the polynomial-of-output in the system growth rate, and the other one is to overcome the serious unknowns in the system and reference signal and the extra system nonlinearities in function control coefficients. Based on the high-gain observer, an adaptive output-feedback controller is successfully designed to guarantee that, for any initial condition of the system, all signals of the closed-loop system are bounded, and the tracking error will be prescribed sufficiently small after a finite time. A numerical example demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is devoted to the semiglobal stabilization via output-feedback for a class of uncertain nonlinear systems. Remark that the systems in question contain an unknown control coefficient which inherently depends on the system output and allow larger-than-two order growing unmeasurable states which is the obstruction of global stabilization via output-feedback. By introducing a recursive reduced-order observer and combining with saturated state estimate, a desired output-feedback controller is explicitly constructed for the systems. Under the appropriate choice of design parameters, the controller can make the closed-loop system semiglobally attractive and locally exponentially stable at the origin. A simulation example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper aims to establish a novel adaptive event-triggered scheme of global output-feedback stabilization for nonlinear systems with unmeasurable state dependent growth under the architecture with network communication among sensor, observer-based controller and actuator. To suppress the inherent system nonlinearities (reflected by the polynomial-of-output growth rate), a dynamic gain is integrated into event-triggered scheme. While reducing execution as much as possible to maintain efficiency, it is critical to ensure timely information transmission and control updating for the effectiveness of adaptive mechanism. To this end, double-side asynchronous adaptive event-triggering mechanisms, instead of a one-side or (double-side) synchronous one, are delicately designed by fully exploiting the dynamic gain, upon which adaptive event-triggered output-feedback control is established to ensure the system states to converge to zero. Particularly, the time of evaluation suspension and the threshold parameter for the events on system output and observer states are not fixed but rather online adjusted with the alteration of dynamic gain, to make sure to counteract the influence of sampling/actuation error. Unlike the related results, the adaptive event-triggering mechanisms render the inter-execution times determined purely according to system behavior and without explicit upper-bound limit, which retain the desired potential in execution reducing. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers the global stability and stabilization of more general stochastic nonlinear systems. Due to the absence of the conventional assumptions (e.g., Lipschitz condition), the stochastic nonlinear systems under investigation may have more than one weak solution. However, the most associated results are only applicable to the stochastic systems having a unique strong solution, and therefore, it is meaningful to refine and extend the relevant concepts and methods to the more general case. In this paper, the concepts of stochastic stability in the more general sense are first introduced to cover the stochastic nonlinear systems having more than one weak solution. Then, the generalized stochastic Barbashin–Krasovskii theorem and LaSalle theorem are established, which present the criterions of stochastic stability for more general stochastic nonlinear systems. As one of the main contributions in this paper, we rigorously prove the generalized stochastic Barbashin–Krasovskii theorem. Moreover, based on the generalized theorems, the output-feedback and state-feedback stabilization are accomplished respectively for two classes of high-order stochastic nonlinear systems under rather weaker assumptions comparing to the existing literature. [Copyright &y& Elsevier]

This paper addresses the event-triggered output-feedback stabilization for a class of uncertain nonlinear systems. Essentially different from the related literature, the systems under investigation allow large uncertainties (whose size is unknown) coupling to unmeasurable states, which requires to integrate a comprehensive output-feedback compensation mechanism into the event-triggered philosophy and would bring substantial challenges to the event-triggered control. To solve the problem, a novel scheme of adaptive event-triggered output-feedback control is established for the systems. Specifically, an adaptive dynamic gain is introduced not only to counteract the large uncertainties but also to cope with the execution/sampling error. Then, an adaptive event-triggered output-feedback controller based on dynamic-gain observer is constructed to guarantee global convergence for the resulting closed-loop system while reducing execution/sampling. Particularly, the event-triggering mechanism involved is delicately designed by fully exploiting the dynamic gain, to enable the negative influence of the gain through the execution error to be counteracted (no matter how large the gain becomes) while precluding infinitely fast execution which violates the implementation of event-triggered controller in practice. Correspondingly, a distinctive analysis pattern is implemented for the closed-loop performance, due to the coupling of the dynamic-gain compensation mechanism and the event-triggering mechanism. Moreover, for more efficient resource saving, we further show the feasibility of adaptive dynamic event-triggering mechanism for the desired stabilization. [ABSTRACT FROM AUTHOR]

This paper considers the global output-feedback stabilization for a class of uncertain strict-feedback nonlinear systems. Besides the unavailability of system state, the nonlinear systems allow unknown control directions and non-parametric uncertainties, which are both arguably the most severe structural uncertainties. Even so, a refined stabilization objective with prescribed convergence rate is pursued in this paper. To solve the control problem, a new output-feedback scheme is established by introducing delicate time-varying gains and utilizing a filtered transformation, where the time-varying gains are pivotal for counteracting the structural uncertainties and guaranteeing the prescribed performance. By forcing certain system signals to evolve within a pre-specified funnel, the designed output-feedback controller can guarantee the system state to converge to zero with prescribed rate. Moreover, unlike the common backstepping design for strict-feedback systems, the established scheme can effectively prevent the computation complexity for controller construction from extremely increasing with system dimension. [ABSTRACT FROM AUTHOR]