Publications

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing the transmission security, a logarithmic-type encoding-decoding mechanism is introduced for each sensor node so as to encode the transmitted data with a finite number of bits. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state by only utilizing the data from the local sensor node and its neighbors, where the proposed filter scheme is truly distributed with desirable scalability. Then, a new ellipsoid-based fusion rule is developed for the designed set-membership filters in order to form the fused ellipsoidal set that has a globally smaller volume than all local ellipsoidal sets. With the aid of the mathematical induction technique, the set theory, and the convex optimization approach, sufficient conditions are derived for the existence of the desired distributed set-membership filters and the fusion weights. Then, the filter parameters and the fusion weights are acquired by solving a set of constrained optimization problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed fusion filtering algorithm.

This paper is concerned with event-triggered output feedback dissipative control of network-based systems. A novel distributed discrete event-triggered control strategy, in which whether or not the sampled data should be transmitted is determined by a pre-specified event, is proposed by introducing distributed discrete event-triggered mechanisms. Under this strategy, a novel dissipative control protocol is presented, with which the closed-loop system can be transformed into a time-delay system. Then, by Lyapunov-Krasovskii functional theory, a new dissipative criterion is established such that the resulting system is (Q, S, R)-dissipative. Correspondingly, based on this condition, the design method of the output feedback dissipative controller is proposed. An illustrative example is given to show the effectiveness and feasibility of the proposed method.

The stability robustness of a discrete linear time-invariant system is analysed. Based on a discrete Lyapunov stability approach, fundamental criteria for testing the stability robustness of autonomous systems are derived and applied to the robustness analysis of multivariable feedback systems. The element bounds for the allowed perturbations are presented.

Fixed-time and prescribed-time consensus control can bring an explicit estimate of the settling time without dependence on initial conditions, which is important in providing control engineers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> system information. This article aims at presenting a survey of recent trends and methodologies of fixed-time and prescribed-time consensus control in multiagent systems. First, some typical fixed-time consensus results are reviewed. Despite the advantage in deriving a fixed settling time bound, fixed-time consensus controllers usually result in a conservative estimate of the bound and a large magnitude of initial control input, which in turn show the necessity of designing prescribed-time consensus controllers. Second, characteristics and controller design of (practical, respectively) prescribed-time consensus are provided in detail. Particularly, representative time-varying function-based controllers are presented, by which (practical, respectively) consensus can be achieved in prescribed time. Third, applications of fixed-time and prescribed-time consensus control in mobile robots and smart grids are illustrated in case studies. Finally, several challenging issues in prescribed-time consensus control are discussed for future research.

Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sig... | Find, read and cite all the research you need on Tech Science Press

DeepSeek, a Chinese Artificial Intelligence (AI) startup, has released their V3 and R1 series models, which attracted global attention due to their low cost, high performance, and open-source advantages. This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts, the mainstream Large Language Model (LLM) paradigm, and the DeepSeek paradigm. Subsequently, the paper highlights novel algorithms introduced by DeepSeek, including Multi-head Latent Attention (MLA), Mixture-of-Experts (MoE), Multi-Token Prediction (MTP), and Group Relative Policy Optimization (GRPO). The paper then explores DeepSeek engineering breakthroughs in LLM scaling, training, inference, and system-level optimization architecture. Moreover, the impact of DeepSeek models on the competitive AI landscape is analyzed, comparing them to mainstream LLMs across various fields. Finally, the paper reflects on the insights gained from DeepSeek innovations and discusses future trends in the technical and engineering development of large AI models, particularly in data, training, and reasoning.

In leader–follower networks, leader selection is an important issue, and efficient schemes of selecting a group of leaders are sought to guarantee the desired coordination performance. In this paper, we study the leader selection problem under switching topologies, and aim to minimize the number of leaders to ensure the consensus of high-order multiagent systems with antagonistic interactions, which are usually represented by negative edge weights on a communication graph. First, as the basis of leader selection, sufficient conditions including average dwell time bounds for each possible topology and a mild network connectivity assumption are derived for followers to reach the expected property. Second, by applying the derived consensus criterion, metrics for identifying leaders are established via the submodular optimization method, and the supermodularity of the metrics is proved in the digraph case. Third, by utilizing the established metrics, a leader selection scheme with two polynomial-time algorithms is presented to determine leaders dynamically, and the optimality of the returned solution has a provable guarantee. Finally, the performance of the proposed leader selection scheme is demonstrated by numerical examples.

In this paper, time-delay effect on continuous approximation of sliding mode control (SMC) systems is studied in depth. The switching band that bound the zigzagging behaviors due to control switching around the switching manifold is constructed. Robust control theory is employed to develop a sufficient condition for the asymptotical stability of the system. Simulation is conducted to verify the theoretical results.

In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor-critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

This chapter addresses network-based heading controlHeading control and rudder oscillation reduction for a UMVUnmanned Marine Vehicles (UMVs) equipped with single rudder in network environments. A novel network-based model is first established by constructing a heading controlHeading control error system and purposely dropping some control input packets, which are received by a steering machine. Then a stabilization criterion is derived to guarantee the heading angle tracking performance and to reduce the oscillation of the rudder angle.

This paper investigates the real-time estimation on the state-of-charge (SoC) and state-of-health (SoH) of lithium-ion (Li-ion) batteries for the purpose of achieving reliable, safe, and efficient use of batteries. Three terminal sliding-mode observers (TSMOs) are designed; each observer is used to estimate one variable of a Li-ion cell for developing a real-time SoC estimation algorithm. To estimate the SoH, two additional TSMOs are subsequently presented. Finally, a set of complete estimation algorithms for SoC and SoH are formulated. The output injection signals of the proposed TSMOs are designed to be continuous. This can attenuate chattering that exists in the traditional sliding-mode observers and simplify the estimation algorithms. The main advantage of the proposed algorithms is eliminating the low-pass filter in the estimation algorithms. Therefore, higher estimation accuracy and faster response speed are obtained. The proposed methods are tested and evaluated using the acquired dynamic stress test and federal urban driving schedule test data, which demonstrate the effectiveness and feasibility.

This brief deals with the problem of global asymptotic stability for a class of delayed neural networks. Some new Lyapunov-Krasovskii functionals are constructed by nonuniformly dividing the delay interval into multiple segments, and choosing proper functionals with different weighting matrices corresponding to different segments in the Lyapunov-Krasovskii functionals. Then using these new Lyapunov-Krasovskii functionals, some new delay-dependent criteria for global asymptotic stability are derived for delayed neural networks, where both constant time delays and time-varying delays are treated. These criteria are much less conservative than some existing results, which is shown through a numerical example.

This chapter deals with the consensus tracking problem for a multi-agent system with nonholonomic chained-form dynamics. A distributed observer is first proposed for each follower to estimate the state and the input of the leader simultaneously in fixed-time under...

The robust stability problem of linear time-delay systems with norm-bounded uncertainty is further investigated by using a refined discretized Lyapunov functional approach. A new stability criterion is derived. The computational requirement is reduced for the same discretization mesh. Examples show that the results obtained by this new criterion significantly improve the estimate of the stability limit over some existing results in the literature.

Most of the existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This article addresses the problem of indoor localization by implementing distributed set-membership filtering based on a received signal strength indicator (RSSI) under unknown-but-bounded process and measurement noises. First, the transmit power and the path-loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in RSSI-based localization. Since the localization process of trilateration is susceptible to inaccuracy caused by the noise-affected distance measurements, a convex optimization method is then developed to obtain the state ellipsoid estimation under the unknown-but-bounded noises. Third, a recursive algorithm is established to compute the global ellipsoid that guarantees to locate the true target at every time step. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed set-membership filtering method for indoor localization.