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Among many damage imaging methods based on Lamb wave, phased array Delay and Sum (DAS) has attracted much attention because of its high efficiency. However, inherent dispersion effect leads to the inaccurate acquisition of time-domain features, which affects imaging accuracy. The imaging index based on the amplitude of scattering wave ignores the interaction between Lamb wave and damage, which is difficult to reflect the physical characteristics of damage scattering, and further affects imaging accuracy. We use the physical characteristic parameter-reflectivity of the damage surface as the imaging index and establish an inverse scattering model of damage in frequency domain with dispersion relationship. The simulation and experiment results show that the DAS method combined with the frequency-domain inverse scattering model can realise the high-quality imaging of the damage in the isotropic waveguide structure. [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]

This paper considers the global finite-time output-feedback stabilization for a class of uncertain nonlinear systems. Comparing with the existing related literature, two essential obstacles exist: On the one hand, the systems in question allow serious parametric unknowns and serious time variations coupling to the unmeasurable states, which is reflected in that the systems have the unmeasurable states dependent growth with the rate being an unknown constant multiplying a known continuous function of time. On the other hand, the systems possess remarkably inherent nonlinearities, whose growth allows to be not only low-order but especially high-order with respect to the unmeasurable states. To effectively cope with these obstacles, we established a time-varying output-feedback strategy to achieve the finite-time stabilization for the systems under investigation. First, a time-varying state-feedback controller is constructed by adding an integrator method, and by homogeneous domination approach, a time-varying reduced-order observer is designed to precisely rebuild the unmeasurable states. Then, by certainty equivalence principle, a desired time-varying output-feedback controller is constructed for the systems. It is shown that, as long as the involved time-varying gain is chosen fast enough to overtake the serious parametric unknowns and the serious time variations, the output-feedback controller renders that the closed-loop system states converge to zero in finite time. Copyright © 2017 John Wiley & Sons, Ltd. [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 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]

This paper considers the global stabilization via time-varying output-feedback for a class of stochastic nonlinear systems. Different from the related existing literature, the systems possess serious uncertainties/unknowns which are reflected in the uncertain control coefficient and the unknown growth rate of the unmeasured states dependent growth. To deal with these serious uncertainties/unknowns, a time-varying framework, rather than an adaptive one, is proposed in this paper since the latter has not been successfully employed in the topic. Detailedly, due to the absence of precise information on the control coefficient, time-varying K-filters are introduced to construct the unmeasured system states. By a delicate time-scaling transformation, a time-scaled entire system is derived, and from which, the design scheme of a time-varying output-feedback controller is developed by backstepping method and time-varying technique. It is shown that all signals of the resulting closed-loop system converge to zero a.s., and furthermore, when serious time-variations exist as well, it suffices to find a fast enough time-varying gain for the control design scheme. A simulation example is given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

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]

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]

This article is devoted to the optimal protocol for the leader-following consensus of second-order multiagent systems. Remarkably, the global consensus cost functional and the directed topology are pregiven, and the optimal protocol to be sought is distributed. This makes the Riccati-based strategies inapplicable, by which merely centralized protocol can be derived. As the main contribution of this article, an effective strategy of seeking a distributed optimal protocol is proposed for second-order agents over the digraph of the directed tree. Detailedly, the feasibility of distributed optimal protocol is first affirmed, that is, the existence of optimal gain parameters can be guaranteed. Then, by recursively deriving the completely explicit formulas of the consensus errors of relative position and velocity, an online implementable algorithm is developed to achieve the parameterization of the cost functional, that is, to obtain the explicit formula of the cost functional with respect to gain parameters of all agents. Lastly, the desired optimal gain parameters are obtained by minimizing the explicit formula. [ABSTRACT FROM AUTHOR]

Summary: Adaptive output‐feedback control is investigated for a class of uncertain damped wave equations. The remarkable features of the system in question are as follows: (i) Unknown damping is allowed at the boundary; (ii) The boundary measurement is corrupted by an unknown harmonic disturbance. These features make the control problem essentially different from those in the related literature. To solve the problem, a novel adaptive output‐feedback control scheme is established for the system. Specifically, an ingenious observer is first introduced to reconstruct the unmeasured system state in the space domain. Based on this, a certain compensation mechanism is designed for the unknown damping coefficient and output disturbance by adaptive technique based on projection operator. Then, an output‐feedback controller is successfully designed by the backstepping method for ODEs, which guarantees that all states of the resulting closed‐loop system are bounded, and the original system state ultimately converges to an arbitrarily prescribed small neighborhood of the origin. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is devoted to the boundary stabilisation via event-triggered output-feedback for a class of 2 × 2 hyperbolic systems. Different from the closely related works, the system parameters are spatially varying and only one boundary measurement is available for feedback, which brings essential difficulties to the analysis and synthesis of event-triggered control. To solve the problem, by using the time-varying strategy and the infinite-dimensional backstepping technique, a novel event-triggered output-feedback controller is designed for the systems. Particularly, a time-varying event-triggering mechanism, with an extra decaying signal introduced into the threshold, is determined. It is rigorously shown that the designed event-triggered controller can guarantee that all the states of the resulting closed-loop system exponentially converge to zero in the sense of L 2 norm while no Zeno phenomenon occurs. Two simulation examples are given to illustrate the effectiveness of the established boundary control strategy. [ABSTRACT FROM AUTHOR]

In the near future, plug-in electric vehicles (PEVs) will dominate the car market. The peak-valley difference would increase greatly due to the random nature of PEVs charging behaviors. In this paper, a new problem on studying the dynamic economic/emission dispatch (DEED) including PEVs for peak shaving and valley filling is proposed, and furthermore, the effect caused by different vehicle-to-grid (V2G) and grid-to-vehicle (G2V) loads on DEED is analyzed. The optimization model of the problem is constructed including several practical constraints, such as power flow constraints and ramp rate limits. The battery degradation cost is also included, which reshapes the objective function of DEED. Based on the model, a strategy for DEED including PEVs for peak shaving and valley filling is given, and a multiobjective optimization algorithm is adopted to solve the proposed problem. To demonstrate the feasibility and effectiveness of the proposed strategy, three DEED cases are considered under different V2G power. The analysis of the effect on fuel cost and emission caused by different V2G power shows that the proposed strategy can effectively reduce the emission and cut down the investment in peak load plants. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective hybrid bat algorithm is proposed to solve the combined economic/emission dispatch problem with power flow constraints. In the proposed algorithm, an elitist nondominated sorting method and a modified crowding-distance sorting method are introduced to acquire an evenly distributed Pareto Optimal Front. A modified comprehensive learning strategy is used to enhance the learning ability of population. Through this way, each individual can learn not only from all individual best solutions but also from the global best solutions (nondominated solutions). A random black hole model is introduced to ensure that each dimension in current solution can be updated individually with a predefined probability. This is not only meaningful in enhancing the global search ability and accelerating convergence speed, but particularly key to deal with high dimensional systems, especially large-scale power systems. In addition, chaotic map is integrated to increase the diversity of population and avoid premature convergence. Finally, numerical examples on the IEEE 30-bus, 118-bus and 300-bus systems, are provided to demonstrate the superiority of the proposed algorithm. [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 article investigates the event‐triggered stabilization for a class of hyperbolic PDE‐ODE cascade systems with nonlocal term. The presence of unknown parameters in the system brings technical challenges from design to analysis. This compels us to integrate certain powerful dynamic compensation into event‐triggering control mechanism. To solve the problem, by adaptive technique, a compensation strategy is developed to counteract the parametric uncertainties. Then, a desired event‐triggered controller is delicately designed to guarantee that all the signals in the closed‐loop system are bounded and the original system states converge to zero in the L2 norm. Particularly, the event‐triggering mechanism with an additional dynamic signal in the threshold is devised to overcome the negative influence of execution error, while ensuring a positive lower bound for the interexecution intervals. Finally, a simulation example is given to illustrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper is devoted to the output-feedback stabilisation for a class of wave equations. Remarkably, only one boundary measurement corrupted by disturbance is available for feedback, and the boundary condition at the control end is dynamic, rather than static. This renders the control problem more challenging than those in the related literature, largely because certain reconstruction and compensation mechanisms respectively for the state and disturbance are necessary but difficult to integrate with the control design for dynamic boundary condition. For this, a powerful adaptive output-feedback stabilising scheme is proposed. Specifically, a delicate observer is constructed for the state in the space domain, with which a dynamic estimate for the disturbance is incorporated. Then, a desired output-feedback controller is designed by backstepping method, which renders the states of the original system and observer to converge to zero, while the estimate of the unknown disturbance converges to its true value. [ABSTRACT FROM AUTHOR]

We present a hybrid metaheuristic optimization algorithm for solving economic dispatch problems in power systems. The proposed algorithm, based on bat algorithm, combines chaotic map and random black hole model together. Chaotic map is used to prevent premature convergence, and the random black hole model is helpful not only in avoiding premature convergence, but also in increasing the global search ability, enlarging exploitation area and accelerating convergence speed. The pseudocode and related parameters of the proposed algorithm are also given in this paper. Different from other related works, the costs of conventional thermal generators and random wind power are both included in the cost function because of the increasing penetration of wind power. The proposed algorithm has no requirement on the convexity or continuous differentiability of the cost function, although the effect on fuel cost, caused by the underestimation and overestimation of wind power, is included. This makes it feasible to take more practical nonlinear constraints into account, such as prohibited operating zones and ramp rate limits. Three test cases are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Conventional critically refracted longitudinal (L CR) wave technique seriously relies on the first critical angle (θ C R ), showing poor adaptability to the wave velocity variations of materials in defect and stress characterization. We propose an enhanced strategy based on the phased array ultrasonic, termed PA-L CR. Three typical beam profiles, deflection, focusing and their combination, are studied on the excitation of L CR wave. With one fixed wedge and time delay law, the wave can be excited within a wild angle range relative to θ C R , about ± 7 ° for −6 dB amplitude, and the adaptable longitudinal wave velocity is ranged about 6000 m/s. In addition, a calculation method of the L CR wave velocity is also proposed and verified. • A phased array ultrasonic-based enhanced strategy, PA-L CR , is proposed for L CR wave. • With one wedge and time delay law, L CR wave can be excited in different materials. • The adaptable first critical angle and P wave velocity range about 20 ° and 6000 m/s. • A new method to calculate the L CR wave velocity is proposed and verified. • PA-L CR strengthens the convenience and performance of L CR characterization. [ABSTRACT FROM AUTHOR]

Economic dispatch can promote the economic operation of power systems, and reactive power dispatch can ensure the safe and reliable operation of power systems. Essentially different from the related literature with separately optimizing the two dispatches, this paper proposes the collaborative optimization strategy of dynamic grid dispatch to optimize economic dispatch and reactive power dispatch simultaneously. The strategy is devoted to analyzing the internal relationship of the two dispatches, and establishing a collaborative optimization model to complete the collaborative optimization of the two dispatches. Moreover, multi-objective hybrid bat algorithm is improved by an unbalanced power distribution method, which is suitable to solve dynamic grid dispatch problem. Finally, wind power predicted by neural network is integrated in the collaborative optimization of dynamic grid dispatch, and the influence of wind power integration on grid dispatch is studied. The numerical examples on the IEEE 30-bus are provided to demonstrate the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]