Yijing Wang

Bio: Yijing Wang is an academic researcher from Tianjin University. The author has contributed to research in topics: Linear system & Exponential stability. The author has an hindex of 24, co-authored 169 publications receiving 2110 citations.

Adaptive Fault-Tolerant Tracking Control for Linear and Lipschitz Nonlinear Multi-Agent Systems

Abstract: This paper considers the problem of fault-tolerant tracking control for linear and Lipschitz nonlinear multi-agent systems subject to actuator faults and the leader's bounded unknown input. The communication topology is the undirected subgraph with directed connections between the leader and the followers. Based on the relative states of neighbors and a general actuator fault model, an adaptive fault-tolerant control protocol is proposed to compensate for the failure effects on consensus tracking where the feedback matrices update the parameters by the online estimation of actuator faults. The criteria of reaching consensus tracking despite the actuator faults for both linear and Lipschitz nonlinear agents are derived, respectively. Finally, two examples are included to illustrate the theoretical results.

Brief paper: Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation

Abstract: In this paper, the problem of robust fault tolerant control for a class of singular systems subject to both time-varying state-dependent nonlinear perturbation and actuator saturation is investigated. A sufficient condition for the existence of a fixed-gain controller is first proposed which guarantees the regularity, impulse-free and stability of the closed-loop system under all possible faults. An optimization problem with LMI constraints is formulated to determine the largest contractively invariant ellipsoid. An adaptive fault tolerant controller is then developed to compensate for the failure effects on the system by estimating the fault and updating the design parameter matrices online. Both of these two controllers are in the form of a saturation avoidance feedback with the advantage of relatively small actuator capacities compared with the high gain counterpart. An example is included to illustrate the proposed procedures and their effectiveness.

Disturbance Observer-Based Integral Sliding-Mode Control for Systems With Mismatched Disturbances

Abstract: This paper develops the disturbance observer-based integral sliding-mode control approach for continuous-time linear systems with mismatched disturbances or uncertainties. The disturbance observer is proposed to generate the disturbance estimate, which can be incorporated in the controller to counteract the disturbance. With the help of the proposed disturbance observer, both the memoryless and memory-based integral sliding surfaces and integral sliding-mode controllers are developed, respectively, and two approaches, i.e., $H_\infty$ control and steady-state output-based approaches, are proposed to design the controller and disturbance rejection gains. Finally, the effectiveness and applicability of the proposed technique are illustrated by a numerical example and a real-time experiment.

A New Method for Stability Analysis of Recurrent Neural Networks With Interval Time-Varying Delay

Abstract: This brief deals with the problem of stability analysis for a class of recurrent neural networks (RNNs) with a time-varying delay in a range. Both delay-independent and delay-dependent conditions are derived. For the former, an augmented Lyapunov functional is constructed and the derivative of the state is retained. Since the obtained criterion realizes the decoupling of the Lyapunov function matrix and the coefficient matrix of the neural networks, it can be easily extended to handle neural networks with polytopic uncertainties. For the latter, a new type of delay-range-dependent condition is proposed using the free-weighting matrix technique to obtain a tighter upper bound on the derivative of the Lyapunov-Krasovskii functional. Two examples are given to illustrate the effectiveness and the reduced conservatism of the proposed results.

Technical communique: Reachable set bounding for delayed systems with polytopic uncertainties: The maximal Lyapunov-Krasovskii functional approach

Abstract: This paper deals with the problem of reachable set estimation for time delay systems subject to both polytopic parameter uncertainties and bounded peak inputs. The maximal Lyapunov-Krasovskii functional is constructed as the pointwise maximum of a family of Lyapunov-Krasovskii functionals. Each functional corresponds to a vertex of uncertain polytope. Some criteria bounding the reachable set are derived. This approach shows great advantages over the traditional methods based on the common Lyapunov functionals. By introducing a modified integral inequality, the limitation imposed on the derivative of time delay being less than one is relaxed. Furthermore, the obtained results involve less computational burden when the number of vertices of the polytope is small. Two examples are given to illustrate the theoretical results.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey

Abstract: In this paper, we mainly review the topics in consensus and coordination of multi-agent systems, which have received a tremendous surge of interest and progressed rapidly in the past few years. Focusing on different kinds of constraints on the controller and the self-dynamics of each individual agent, as well as the coordination schemes, we categorize the recent results into the following directions: consensus with constraints, event-based consensus, consensus over signed networks, and consensus of heterogeneous agents. We also review some applications of the very well developed consensus algorithms to the topics such as economic dispatch problem in smart grid and k -means clustering algorithms.

A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

Abstract: Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized The relationship among stability results in different forms, such as algebraic inequality forms, \(M\) -matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed Concluding remarks and future directions of stability analysis of recurrent neural networks are given