Fei Meng

Collective intelligence in biological groups can be employed to inspire the control of artificial complex systems, such as swarm robotics. However, modeling for the social interactions between individuals is still a challenging task. Without loss of generality, we propose a deep attention network model that incorporates the principles of biological Hard Attention mechanisms, that means an individual only pay attention to one or two neighbors for collective motion decision in large group. The model is trained by the collective movement data of five rummy-nose tetra fish (Hemigrammus rhodostomus). The structure of the model enforces individual agents to consider information from at most two neighboring agents. Meanwhile, the model can reveal hidden locations, where highly influential neighbors frequently appear. These findings demonstrate that the proposed Hard Attention Model aligns with the information processing mechanisms, which is observed in fish schooling. Experimental results indicate that the model exhibits a strong ability to decouple sparse information for collective movement with robust metrics. It can also perform excellent scalability in different group sizes. The simulation and real robots experiment show that the model provides a powerful tool for analyzing multi-level behaviors in complex systems and offers significant insights for the distributed control of swarm robotics.

This article presents a model predictive control (MPC) strategy that leverages neural ordinary differential equations (NODEs) to address the persistent issue of model mismatch in nonlinear systems. A residual model based on NODEs is employed to capture the differences between the actual system dynamics and conventional models. Simultaneously, an approximate model with an uncertainty upper bound, derived from first principles, is introduced. These two models are integrated into a learning‐based MPC framework, effectively decoupling system safety and performance, leading to more reliable control. To enhance model and controller adaptability, we propose an online dynamic learning framework that continuously refines the accuracy of the dynamic model. Compared to traditional learning‐based MPC, the proposed algorithm significantly reduces memory usage. Furthermore, using the theory of invariant sets, we establish the recursive feasibility and stability of the algorithm. The method's effectiveness is demonstrated through addressing nonlinear problems involving modeling errors and stochastic disturbances.

Ultra-wideband technology is recognized as a low-cost and interference-resistant method for indoor vehicle localization, crucial for advancing intelligent transportation systems. Within this domain, the time difference of arrival algorithm finds extensive application. However, the accuracy of indoor three-dimensional (3D) localization is significantly affected by multipath propagation and non-line-of-sight deviations. To improve localization accuracy, this study proposes an innovative method utilizing the graph attention network framework. This method extracts relevant features and employs attention mechanisms to differentially weight individual nodes; it adeptly captures crucial spatial relationships among nodes, thereby mitigating the effects of multipath propagation and non-line-of-sight deviations, ultimately achieving finer indoor vehicle localization. Experimental results from tests on line-of-sight and non-line-of-sight data sets illustrate the significant improvement in indoor vehicle 3D localization accuracy afforded by the proposed graph attention network–based method. Compared to the commonly used built-in error-state Kalman filter algorithm in commercial ultra-wideband applications and alternative deep learning algorithms, the proposed method achieves error reductions ranging from 60.58% to 84.16% in line-of-sight environments and from 65.59% to 95.21% in non-line-of-sight environments.

In this paper, the stability analysis and controller design of a 2-D switched system with random disturbances are studied. First, based on the Fornasini-Marchesini local state-space model, a random discrete 2-D switched system model is proposed. Secondly, based on the method of switching quadratic Lyapunov function, Schur complement and linear matrix inequality the criterion of the sufficiency of the asymptotical stability under any switching signal is established. The extended average residence time method is used to obtain a sufficient criterion of the mean square exponential stability of the random discrete two-dimensional switching system under restricted switching conditions. According to the stability analysis results, the conditions for the existence of the state feedback controller are given and the corresponding state feedback controller is designed. Finally, two examples are given to illustrate the effectiveness of the proposed method.

This paper proposes an electronic search algorithm-based transient overvoltage suppression method for the transient overvoltage problem caused by DC faults during the outgoing transmission of new energy clusters from high-voltage DC. Firstly, the relationship between the control parameters of the rotor-side converter and the transient voltage rise of the doubly-fed wind turbine is determined by combining the transient overvoltage principle; secondly, the control parameters are optimized and searched based on the electronic search algorithm. Finally, by simulation, the electronic search algorithm is proved to be effective in terms of convergence and parameter optimization as well as the applications of transient overvoltage suppression.

Aiming to boost up the closed-loop capability of the track tracking model predictive control(MPC) controller, this paper presents a predictive control algorithm that is based on sparse Bayesian learning, which is derived from the original vehicle kinematics and MPC of the track tracking controller. The algorithm learns the uncertainty of the system on the basis of the input and output data gathered and adapts the predictive model to enhance the accuracy of the track tracking. At last, the validity of the controller was proven by simulating and the simulation results indicate that the controller decreased the trajectory tracking error and raised the accuracy of track tracking as compared to the traditional MPC controller.

In this paper, the stability analysis and controller design of two-dimensional (2-D) switched system with random disturbances are studied. First, based on the Fornasini–Marchesini local state space model, a random discrete 2-D switched system model is proposed. Secondly, based on the method of switching quadratic Lyapunov function, Schur complement and linear matrix inequality the criterion of the sufficiency of the asymptotically stable under any switching signal is established. The extended average residence time method is used to obtain a sufficient criterion of the mean square exponential stability of the random discrete 2-D switched system under restricted switching conditions. According to the stability analysis results, the conditions for the existence of the state feedback controller are given and the corresponding state feedback controller is designed. Finally, two examples are given to illustrate the effectiveness of the proposed method.