Search

This article investigates $H_{\infty }$ synchronization of delayed neural networks under a receding horizon scheme, where two types of interval time-varying delays are considered according to whether the lower bound of the delay derivative is known or not. Note that a receding horizon synchronization law can be regarded as an optimization solution at each timeslot to a minimaxization problem related closely with a certain cost functional. In this article, two cost functionals with some delay-dependent matrices are introduced, respectively, for the two types of time delays. In order to obtain less conservative conditions, a novel inequality on quadratic polynomial functions is established, which includes some existing ones as its special cases. Based on the novel inequality, two sufficient conditions are derived to design the terminal weighting matrices of the cost functionals such that the resulting synchronization error system can be stabilized with a prescribed infinite horizon $H_{\infty }$ performance level. Finally, three numerical examples are used to demonstrate the validity of the proposed results. [ABSTRACT FROM AUTHOR]

This correspondence is concerned with network-based H∞ filtering for discrete-time systems. The output signals of the system under consideration are transmitted to the filter through a constraint communication network, which usually leads to network-induced delays and packet dropouts. By introducing a logic data packet processor to choose the newest data signal from the network to actuate the filter, network-induced delays and packet dropouts are modeled as a Markov chain taking values in a finite set. As a result, the filter to be designed is modeled as a Markov jumping linear filter. By introducing some slack matrix variables in terms of probability identity, a less conservative bounded real lemma (BRL) is derived to ensure that the filtering error system is stochastically stable with a prescribed H∞ level. Based on this BRL, suitable H∞ filters are designed by employing a cone complementary approach. A practical example on the Leslie model about some certain pest's structured population dynamics is given to show the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

This article presents an additive-state-decomposition-based repetitive control (RC) method for a class of nonlinear systems with nonintegral-chain form and multiple mismatched disturbances. The key idea of this method is to decompose the original tracking problem for an uncertain nonlinear system into two tractable subproblems, namely RC problem for a primary linear time-invariant (LTI) periodic system and robust stabilization problem for a nonlinear secondary system. By taking the equivalent total effect of the multiple disturbances as one matched total disturbance, an improved linear extended state observer (ILESO) is constructed to estimate and compensate for the adverse effect of the multiple disturbances. The method has three significant features: First, it builds a bridge between the existing RC methods for LTI systems and nonlinear systems and, thus, broadens the application scope of RC; second, besides the known information of the model being used to the design of ILESO, the periodic nonlinearity in the plant is extracted to be treated by RC, which enhances both the transient and steady-state performances; and third, the ILESO-based control scheme and the RC law are designed separately, which is flexible for system design. The stability criterion and design procedure are presented in detail. Finally, numerical and experimental results illustrate the validity and superiorities of the method. [ABSTRACT FROM AUTHOR]

This paper presents a linear-extended-state-observer-based repetitive control (RC) method to enhance the disturbance rejection performance and tracking performance for a nonlinear system with aperiodic uncertainties and disturbances. Both design scenarios, i.e., with and without a detailed mathematical model of the plant, are considered, respectively. The proposed method exhibits the following three attractive features. First, instead of tuning the observer gain via the bandwidth idea by trial and error, available information on the plant model is used to design the observer gain and the state-feedback RC gains. As a result, the disturbance rejection ability is elevated. Second, robust stability and learning efficiency are adjusted preferentially through two tuning parameters, which effectively improve both the transient and steady-state performance. Third, the observer gain and state-feedback RC gains are simultaneously optimized through an optimization algorithm, in which an index is introduced to evaluate the overall system performance. The design procedure of the system is presented in detail. Finally, an application to tracking control of a chuck-workpiece system demonstrates the validity of the proposed method. Comparisons with the conventional RC method, the standard extended-state-observer-based RC method, and a linear active-disturbance-rejection-control-based RC method show that the proposed method can achieve the best disturbance rejection performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of cluster formation control for a networked multi-agent system (MAS) in the simultaneous presence of aperiodic sampling and communication delays. First, to fulfill multiple formation tasks, a group of agents are decomposed into $M$ distinct and nonoverlapping clusters. The agents in each cluster are then driven to achieve a desired formation, whereas the MAS as a whole accomplishes $ M $ cluster formations. Second, by a proper modeling of aperiodic sampling and communication delays, an aperiodic sampled-data cluster formation protocol (CFP) is delicately constructed such that the information exchanges among neighboring agents only occur intermittently at discrete instants of time. Third, a detailed theoretical analysis of cluster formability is carried out and a sufficient and necessary condition is provided such that the system is $M$-cluster formable. Furthermore, a discontinuous Lyapunov functional approach is developed to derive a design criterion on the existence of an admissible sampled-data CFP. Finally, numerical simulations on a team of nonholonomic mobile robots are given to illustrate the effectiveness of the obtained theoretical result. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed cooperative longitudinal control of automated vehicle platoons subject to a variety of uncertainties, including unknown engine time lags, external disturbances, measurement noises, and actuator anomaly in follower vehicles as well as unknown leader control. First, a unified framework is proposed for accomplishing resilient vehicle platooning, which empowers longitudinal vehicle state estimation, anomaly signal estimation and compensation, and adaptive platoon controller design to be addressed in a comprehensive way. Second, a novel scalable platooning control design approach is developed to guarantee desired platoon stability and resilience over generic communication topologies and various spacing policies. A salient feature of the approach is that the design procedure does not depend on any global information of the associated topology, and thus preserves essential scalability for large and/or size-varying platoons. Third, it is shown that the proposed longitudinal platooning control approach is promising for performing flexible cooperative maneuvers such as platoon splitting and merging that are beyond the capacity of most existing longitudinal platooning strategies. Finally, simulation results for different platoon maneuvers are elaborated to substantiate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Distributed coordination control is the current trend in networked systems and finds prosperous applications across a variety of fields, such as smart grids and intelligent transportation systems. One fundamental issue in coordinating and controlling a large group of distributed and networked agents is the influence of intermittent interagent interactions caused by constrained communication resources. Event-triggered communication scheduling stands out as a promising enabler to strike a balance between the desired control performance and the satisfactory resource efficiency. What distinguishes dynamic event-triggered scheduling from traditional static event-triggered scheduling is that the triggering mechanism can be dynamically adjusted over time in accordance with both available system information and additional dynamic variables. This article provides an up-to-date overview of dynamic event-triggered distributed coordination control. The motivation of dynamic event-triggered scheduling is first introduced in the context of distributed coordination control. Then some techniques of dynamic event-triggered distributed coordination control are discussed in detail. Implementation and design issues are well addressed. Furthermore, this article exemplifies two applications of dynamic event-triggered distributed coordination control in the fields of microgrids and automated vehicles. Several challenges are suggested to direct the future research. [ABSTRACT FROM AUTHOR]