Lei Ma

Bio: Lei Ma is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Singular perturbation & Discrete time and continuous time. The author has an hindex of 2, co-authored 3 publications receiving 39 citations.

Dynamic Event-Triggered State Estimation for Discrete-Time Singularly Perturbed Systems With Distributed Time-Delays

Abstract: This paper is concerned with the state estimation problem for a class of discrete-time singularly perturbed systems with distributed time-delays. During the data transmission through a network channel of limited bandwidth, for the sake of collision avoidance and energy saving, a dynamic event-triggered scheme is employed to schedule the data communication from the sensors to the designed estimator. First, for a given singular perturbation parameter (SPP), by constructing a novel Lyapunov–Krasovskii SPP-dependent functional, sufficient conditions are obtained to guarantee the exponentially mean-square ultimate boundedness of the error dynamics of the state estimation. Furthermore, in the case that the SPP does not exceed a predefined upper bound, a design algorithm is developed for the desired state estimator ensuring that the error dynamics is exponentially mean-square ultimately bounded. In this case, by solving certain matrix inequalities, the estimator gain is characterized without needing to know the exact SPP (as long as it stays below the given upper bound). Moreover, the ultimate bound of the error dynamics is estimated. Finally, simulation results are given to confirm the validity and advantages of the proposed design scheme of the state estimator.

Stability analysis and stabilization synthesis of singularly perturbed switched systems: An average dwell time approach

Abstract: Stability analysis and stabilization synthesis problems of the singularly perturbed switched systems (SPSSs) are investigated in this paper. First, stability of the SPSS with stable subsystems is discussed by using an average dwell time approach with a full-order piecewise Lyapunov function. Then, the result is extended to the situation that not all the subsystems are Hurwitz stable. Furthermore, state feedback controllers are designed when all subsystems are stabilizable, and the design method is extended to the situation that not all subsystems are stabilizable. Finally, two examples are given to demonstrate the validity and effectiveness of the proposed methods.

Event-Triggered Fault Detection for Discrete-Time Singularly Perturbed Systems in Finite-Frequency Domain

Abstract: This paper investigates the fault detection problem for a class of discrete-time singularly perturbed systems under the event-triggered communication protocal. A finite frequency approach is proposed for the achievement of the fault detection objectives. With the help of the proposed fault detection filter, the residual signal for the detection of finite frequency faults can be produced. In addition, the upper bound of the singular perturbation parameter is also taken into consideration in the proposed design method. Finally, a simulation example is provided to show the effectiveness of the proposed algorithm.

Non-Fragile <i>H</i>∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation

Abstract: This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H∞ performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.

A survey on state estimation of complex dynamical networks

Abstract: The analysis and synthesis issues have gained widespread attention for complex dynamical networks (CDNs) over the past few years. Accordingly, some challenges including protocol-based scheduling, s...

Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters

Abstract: This article addresses the investigation of sliding-mode control (SMC) for slow-sampling singularly perturbed systems (SPSs) with Markov jump parameters. As a new attempt, the SMC strategy is considered in the study of discrete-time Markov jump SPSs. Subsequently, in order to design a sliding-mode controller to ensure the stability of the proposed system, a novel integral sliding surface is constructed, and an SMC law is synthesized to ensure the reachability of the sliding surface. Through the utilization of Lyapunov stability and SMC theory, sufficient conditions are derived to ensure the state trajectories of the system are driven to a predefined sliding surface and the closed-loop sliding mode dynamics are stochastically stable. Finally, the applicability of the proposed SMC strategy is verified by a numerical example and a practical electric circuit model.

Adaptive Neural Event-Triggered Control for Discrete-Time Strict-Feedback Nonlinear Systems

Abstract: This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.