This paper investigates the practical fixed-time consensus problem for a multi-agent system in networks with an undirected topology. Both node-based and edge-based fully distributed adaptive protocols are proposed to achieve the practical fixed-time consensus for the single integrator-type multi-agent systems, i.e., fixed-time attractiveness of a residual set as well as asymptotic consensus. Most notably, the settling time estimate can be computed without using any global information, which is distinctive from the existing fixed-time protocols. Furthermore, a scaling technique is applied for the proposed protocol such that the fixed-time attraction region can be contracted by only tuning a scaling parameter and the original settling time estimate remains unchanged. Finally, a numerical simulation example is given to validate the proposed protocols.
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This paper is concerned with distributed sampled-data asynchronous H ∞ filtering for a continuous-time Markovian jump linear system over a sensor network, where jumping instants of system modes and filter modes are asynchronous. A group of sensor nodes are deployed to measure the system׳s output and to collaboratively share the measurement with neighboring nodes in accordance with Markovian switching topologies. First, the measurement on each sensor node is sampled at separate discrete instants and transmitted to a remote filter through a communication network. Network-induced signal transmission delays are incorporated in data transmission channels. Second, distributed sampled-data asynchronous H ∞ filters, governed by a finite piecewise homogeneous Markov process, are delicately constructed. The resultant filtering error system is transformed into a piecewise homogeneous Markovian jump linear system with delays. Third, sufficient conditions on the existence of desired distributed sampled-data asynchronous H ∞ filters are derived such that the filtering error system is stochastically stable with the prescribed weighting average H ∞ performance. Finally, three illustrative examples are given to show the effectiveness and advantage of the proposed theoretical results.
Cyber-physical systems (CPSs), which are an integration of computation, networking, and physical processes, play an increasingly important role in critical infrastructure, government and everyday life. Due to physical constraints, embedded computers and networks may give rise to some additional security vulnerabilities, which results in losses of enormous economy benefits or disorder of social life. As a result, it is of significant to properly investigate the security issue of CPSs to ensure that such systems are operating in a safe manner. This paper, from a control theory perspective, presents an overview of recent advances on security control and attack detection of industrial CPSs. First, the typical system modeling on CPSs is summarized to cater for the requirement of the performance analysis. Then three typical types of cyber-attacks, i.e. denial-of-service attacks, replay attacks, and deception attacks, are disclosed from an engineering perspective. Moreover, robustness, security and resilience as well as stability are discussed to govern the capability of weakening various attacks. The development on attack detection for industrial CPSs is reviewed according to the categories on detection approaches. Furthermore, the security control and state estimation are discussed in detail. Finally, some challenge issues are raised for the future research.
This paper is concerned with a delayed feedback control design for uncertain systems with time-varying input delay. Based on a reduction method, a new control design method is proposed by introducing some relaxation matrices and turning parameters, which can be chosen properly to lead to a less conservative result. A numerical example is given to show the effectiveness and less conservativeness of the method.
This paper is concerned with the problem of robust absolute stability for a class of uncertain Lur'e systems of neutral type. Some delay-dependent stability criteria are obtained and formulated in the form of linear matrix inequalities (LMIs). Neither model transformation nor bounding technique for cross terms is involved through derivation of the stability criteria. A numerical example shows the effectiveness of the criteria
In this paper, we propose a finite-time model reference adaptive controller to attenuate the recoil response of deepwater drilling riser systems following an emergency disconnection. Firstly, a coupled model that characterizes the recoil movements of the riser, wave-excited heave motion, and the frictional resistance due to drilling fluid discharge is developed. Secondly, an adaptive control law is presented, with a portion of the adaptive gain determined through linear matrix inequalities. Thirdly, the stability criteria and updating rules that ensure finite settling time for the recoil control system is established. Then, the effectiveness and robustness of the controller against nonlinear systematic perturbations are verified. It is worth pointing out that: (1) The proposed adaptive controller significantly attenuates the recoil responses of the riser in finite time. (2) Compared to other controllers, the finite-time adaptive controller provides faster convergence for both error states and adaptive gains. (3) Perturbation cases involving nonlinear and unmodeled dynamics are examined, demonstrating the controller’s effectiveness and robustness, which ensures the safety of the drilling riser system.
This paper is concerned with global asymptotic stability for a class of generalized neural networks (NNs) with interval time-varying delays, which include two classes of fundamental NNs, i.e., static neural networks (SNNs) and local field neural networks (LFNNs), as their special cases. Some novel delay-independent and delay-dependent stability criteria are derived. These stability criteria are applicable not only to SNNs but also to LFNNs. It is theoretically proven that these stability criteria are more effective than some existing ones either for SNNs or for LFNNs, which is confirmed by some numerical examples.
In this article, we present a collaborative neurodynamic optimization approach to distributed chiller loading in the presence of nonconvex power consumption functions and binary variables associated with cardinality constraints. We formulate a cardinality-constrained distributed optimization problem with nonconvex objective functions and discrete feasible regions, based on an augmented Lagrangian function. To overcome the difficulty caused by the nonconvexity in the formulated distributed optimization problem, we develop a collaborative neurodynamic optimization method based on multiple coupled recurrent neural networks reinitialized repeatedly using a meta-heuristic rule. We elaborate on experimental results based on two multi-chiller systems with the parameters from the chiller manufacturers to demonstrate the efficacy of the proposed approach in comparison to several baselines.
This note is concerned with the stability problem of linear delay-differential systems of neutral type. A discretized Lyapunov functional approach is developed. The resulting stability criterion is formulated in the form of a linear matrix inequality. For nominal systems, the analytical results can be approached with fine discretization. Numerical examples show significant improvement over approaches in the literature.
In this paper, consensus tracking is investigated for multi-agent systems (MASs) with unknown disturbances. A K∞ function-based control strategy is proposed to
Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in event-triggered consensus of MASs. First, a basic framework of multiagent event-triggered operational mechanisms is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis is made on several event-triggered schemes, including event-based sampling schemes, model-based event-triggered schemes, sampled-data-based event-triggered schemes, and self-triggered sampling schemes. Third, two examples are outlined to show applicability of event-triggered consensus in power sharing of microgrids and formation control of multirobot systems, respectively. Finally, some challenging issues on event-triggered consensus are proposed for future research.
This paper deals with the problem of odor source localization by designing and analyzing a finite-time motion control strategy (FTMCS), which consists of a finite-time parallel motion control algorithm and a finite-time circular motion control algorithm. Specifically, a motion control architecture is first given and includes two important modules: 1) a coordinating control module; and 2) a tracking control module. In the coordinating control module, robots communicate with each other to coordinate their virtual position and virtual velocity such that the virtual velocity consensus and the accurate virtual shape decided by the potential function can be reached within a finite-time interval. In the tracking control module, a finite-time tracking control algorithm is implemented such that the real velocity and the real position of the robot can track the virtual velocity and the virtual position within a finite-time interval. Based on the proposed motion control architecture, a finite-time parallel motion control algorithm that can control a group of robots to trace a plume, is derived. Moreover, a finite-time circular motion control algorithm that can enable the robot group to search for odor clues is also designed. Finally, simulations are worked out to illustrate the effectiveness of the FTMCS for odor source localization.
treatment, and measurement of industrial processes.
The sampled-data H ∞ filtering for a continuous-time Takagi–Sugeno fuzzy system with an interval time-varying state delay is investigated, where the measurement outputs from the plant to the filter are assumed to be sampled at discrete instants with a variable period. Firstly, by means of a newly proposed inequality bounding technique and a new Lyapunov–Krasovskii functional, the fuzzy sampled-data H ∞ filtering performance analysis is carried out such that the resultant filter error system is asymptotically stable with a prescribed H ∞ attenuation performance index. Secondly, sufficient conditions on the existence of fuzzy sampled-data H ∞ filters are derived in the simultaneous presence of the time-varying state delay and the variable sampling period. The proposed bounding inequality lies in its more tightness and alleviates the enlargement of some inverse “coefficients” resulting from the utilization of the well-known Jensen integral inequality. Compared with some existing Lyapunov–Krasovskii functionals, more information about the relationship among the current state and its delayed state is considered. The upper bound of the derivative of the time-varying state delay is not required to be less than one. Different from some existing results in the literature, by applying the proposed results, each different value of such an upper bound (greater than one) leads to a different H ∞ disturbance attenuation level. Finally, a numerical example and a modified continuous stirred tank reactor system are given to show the effectiveness of the proposed results.
The network-based modeling and active control for an offshore steel jacket platform with an active tuned mass damper mechanism is investigated in this chapter. A network-based state feedback control scheme is developed. Under this scheme, the corresponding...
