Publications

This paper considers L2 control for a networked control system with quantized measurement. The system state is periodically sampled and quantized. An event-triggered transmitter is introduced to determine whether or not the current quantized measurement should be transmitted through the communication channel to the controller. In this event-triggered networked control system framework, a networked controller is designed to guarantee the L2 stability of the closed-loop system. Two numerical examples are given to demonstrate the effectiveness of the proposed method. It is shown that the average transmission interval could be increased substantially while the control performance is maintained.

This paper focuses on the stability problem for neutral delay-differential systems with norm-bounded, and possibly time-varying, uncertainty. The time-delay is assumed constant and known. A stability criterion that is formulated in a linear matrix inequality (LMI) form is derived. Numerical examples show that the results obtained by this new criterion significantly improve the estimate of stability limit over some existing results in the literature.

The problem of delay-dependent stability in the mean square sense for stochastic systems with time-varying delays, Markovian switching and nonlinearities is investigated. Both the slowly time-varying delays and fast time-varying delays are considered. Based on a linear matrix inequality approach, delay-dependent stability criteria are derived by introducing some relaxation matrices which can be chosen properly to lead to a less conservative result. Numerical examples are given to illustrate the effectiveness of the method and significant improvement of the estimate of stability limit over some existing results in the literature.

In this paper, we study the existence of smooth local solutionsto Weingarten equations and $\sigma_k$-equations. We will prove that, for $2\le k\le n-1$,the Weingarten equations and the $\sigma_k$-equationsalways have smooth local solutions regardless ofthe sign of the functions in the right-hand side of the equations.We will demonstrate that the associated linearized equations are uniformly ellipticif we choose the initial approximate solutions appropriately.

The design and self‐assembly of pyrene‐modified rhomboidal metallodendrimers R1 – R6 via directional metal‐ligand bonding approach is described. By employing pyrene‐containing 120° di‐Pt(II) acceptor and appropriate 60° dendritic dipyridyl donors, a variety of [ G‐1 ]–[ G‐3 ] pyrene‐modified rhomboidal metallodendrimers with well‐defined shape and size were prepared under mild conditions in high yields. The supramolecular dendrimers were characterized with multinuclear NMR ( 1 H and 31 P) and mass spectrometry (CSI‐TOF‐MS). Isotopically resolved mass spectrometry data support the existence of the pyrene‐modified dendrimers with rhomboidal cavities, and NMR data were consistent with the formation of all ensembles. The shape and size of all rhomboidal dendrimers were investigated with the PM6 semiempirical molecular orbital method. Their primary photochemical properties were studied as well.

This paper is concerned with the state estimation for neural networks with two additive time-varying delay components. Three cases of these two time-varying delays are fully considered: 1) both delays are differentiable uniformly bounded with delay-derivative bounded by some constants; 2) one delay is continuous uniformly bounded while the other is differentiable uniformly bounded with delay-derivative bounded by certain constants; and 3) both delays are continuous uniformly bounded. First, an extended reciprocally convex inequality is introduced to bound reciprocally convex combinations appearing in the derivative of some Lyapunov-Krasovskii functional. Second, sufficient conditions are derived based on the extended inequality for three cases of time-varying delays, respectively. Third, a linear-matrix-inequality-based approach with two tuning parameters is proposed to design desired Luenberger estimators such that the error system is globally asymptotically stable. This approach is then applied to state estimation on neural networks with a single interval time-varying delay. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.

This paper first proposes a discrete event-triggered communication scheme for a class of networked Takagi-Sugeno (T-S) fuzzy systems. This scheme has two main features: 1) Whether or not the sampled state should be transmitted is determined by the current-sampled state and the error between the current-sampled state and the latest transmitted state. Compared with those in a periodic time-triggered communication scheme, the communication bandwidth utilization is considerably reduced while preserving the desired control performance; and 2) it is a discrete event-triggered communication scheme due to the fact that the triggered conditions are only measured and checked at a constant sampling period. Compared with a continuous event-triggered communication scheme, the special hardware for continuous measurement and computation is no longer needed. Second, a networked T-S fuzzy model is delicately constructed, which not only considers nonuniform time scales in the networked T-S fuzzy model and the parallel distributed compensation fuzzy control rules but includes the aforementioned state error as well. Third, a stability criterion and a stabilization criterion about the networked T-S fuzzy system are derived, respectively. The stability criterion and stabilization criterion can provide a tradeoff to balance the required communication resource and the desired performance: Lowering the desired performance allows the network to allocate more limited bandwidth to other nodes in need. Finally, a numerical example is given to show the effectiveness of the proposed method.

This paper developed a parallel computing architecture for high-fidelity virtual coupling simulations. Multi-body train dynamics models considered various nonlinear components including wheel-rail contact, suspensions, and inter-vehicle connections. A virtual coupling controller was developed which can be implemented under various train-to-train communication topologies. The controller also allows existing trains to leave the platoon and new trains to merge into the platoon without re-designing the controller. The parallel computing architecture is also scalable and not limited by: the number of vehicles in each train; the number of trains in each train platoon and the topology of train-to-train communications. A case study by simulating a three-train (18 vehicles in total) platoon on a real-world track section was conducted. The results show that, by using 19 computer cores, parallel computing speed is nearly twice as fast as real-time. Parallel computing is about 17 times faster than serial computing. The results also show that the maximum spacing errors of the follower trains were about 0.22 m. Dynamics results such as wheel-rail contact forces, suspension forces, carbody vibrations and inter-vehicle forces were obtained; these results can be used to conduct system assessments in terms of passenger ride comfort, mechanical wear, etc.

Networked control over data networks has received increasing attention in recent years. Among many problems in networked control systems (NCSs) is the need to reduce control latency and jitter and to deal with packet dropouts. This paper introduces our recent progress on a queuing communication architecture for real-time NCS applications, and simple strategies for dealing with packet dropouts. Case studies for a middle-scale process or multiple small-scale processes are presented for TCP/IP based real-time NCSs. Variations of network architecture design are modelled, simulated, and analysed for evaluation of control latency and jitter performance. It is shown that a simple bandwidth upgrade or adding hierarchy does not necessarily bring benefits for performance improvement of control latency and jitter. A co-design of network and control is necessary to maximise the real-time control performance of NCSs

Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability.The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms.It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering.Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks.First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided.The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed.Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail.Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models.Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models.Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.

This paper is concerned with the problem of robust output feedback controller design for a class of linear systems with uncertain parameters. The objective is to design a controller such that the closed-loop poles are assigned within a specified disc and the steady regulated output covariance is guaranteed to be less than a given upper bound. Using a linear matrix inequality (LMI) approach, the existence conditions of such controllers are derived, and a parametrized characterization of a set of desired controllers (if they exist) is presented in terms of the feasible solutions to a set of LMIs. A procedure is given to select a suitable output feedback controller that minimizes the desired control effort.

This brief is concerned with the problem of neural state estimation for static neural networks with time-varying delays. Notice that a Luenberger estimator can produce an estimation error irrespective of the neuron state trajectory. This brief provides a method for designing such an estimator for static neural networks with time-varying delays. First, in-depth analysis on a well-used reciprocally convex approach is made, leading to an improved reciprocally convex inequality. Second, the improved reciprocally convex inequality and some integral inequalities are employed to provide a tight upper bound on the time-derivative of some Lyapunov-Krasovskii functional. As a result, a novel bounded real lemma (BRL) for the resultant error system is derived. Third, the BRL is applied to present a method for designing suitable Luenberger estimators in terms of solutions of linear matrix inequalities with two tuning parameters. Finally, it is shown through a numerical example that the proposed method can derive less conservative results than some existing ones.