Switching communication topologies may cause instability in vehicle platoons, as vehicle information can be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, it remains a significant challenge to capture the dynamic characteristics of switching topologies and obtain sufficient vehicle information for controller design while ensuring stability. In this study, an observer-based distributed model predictive control (DMPC) framework is developed for vehicle platoons under directed Markovian switching communication topologies. The directed switching communication topology is modeled using a continuous-time Markov chain to characterize its stochastic switching behavior. To estimate the leader vehicle information required for control, a fully distributed adaptive observer is designed, whose estimation performance is robust to randomly switching topologies. A sufficient condition is further derived to guarantee the mean-square stability of the observer error dynamics. Based on the estimated information, a terminal update law is constructed to ensure mean-square consensus, and a string stability constraint is formulated to explicitly enforce predecessor-follower string stability within the DMPC framework. Recursive feasibility and closed-loop stability properties of the resulting control scheme are established. Numerical simulation results demonstrate that the proposed method enhances tracking performance, accelerates convergence, and reduces control effort, while maintaining predecessor-follower string stability. With acceleration fluctuations and packet loss, the proposed method reduces the maximum position error by 45%, and improves stability by 39%, demonstrating significant improvements in tracking performance and system stability. Furthermore, the effectiveness of the proposed framework is validated using real-world driving data.

Switching communication topologies can cause instability in vehicle platoons, as vehicle information may be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, capturing the dynamic characteristics of switching topologies and obtaining complete vehicle information for controller design while ensuring stability remains a significant challenge. In this study, we propose an observer-based distributed model predictive control (DMPC) method for vehicle platoons under directed Markovian switching topologies. Considering the stochastic nature of the switching topologies, we model the directed switching communication topologies using a continuous-time Markov chain. To obtain the leader vehicle's information for controller design, we develop a fully distributed adaptive observer that can quickly adapt to the randomly switching topologies, ensuring that the observed information is not affected by the dynamic topology switches. Additionally, a sufficient condition is derived to guarantee the mean-square stability of the observer. Furthermore, we construct the DMPC terminal update law based on the observer and formulate a string stability constraint based on the observed information. Numerical simulations demonstrate that our method can reduce tracking errors while ensuring string stability.

Fault-tolerant control can maintain acceptable performance in faulty systems but may violate safety constraints, posing potential risks. Additionally, model uncertainties present further challenges. In this paper, we propose a novel fault-tolerant probabilistic safe control framework that integrates Gaussian process (GP) regression, fault-tolerant control, and the high-order control barrier function (HOCBF) method. To handle inherent uncertainties, we adopt a probabilistic method that combines control Lyapunov function (CLF) with HOCBF using GP regression. Building on this foundation, we design a fault-tolerant GP-based CLF-HOCBF method to address unknown actuator faults. Furthermore, we establish two theoretical criteria to ensure the probabilistic safety and stability of the proposed control framework. To validate our method, we implement it in the autonomous driving simulator CARLA, demonstrating its effectiveness and competitiveness compared to existing approaches. Note to Practitioners-Control systems should maintain stability and performance in the presence of model uncertainties, especially by ensuring during faults until appropriate engineering measures are implemented. However, existing methods on addressing uncertainties will influenced by observation noise and imperfect measurements, and current fault-tolerant control strategies struggle to handle safety-critical control problems. Given this, this paper suggests a new approach using GP regression and HOCBF, considering the cases of unknown system dynamics and actuator bias faults. Accordingly, sufficient conditions are established for guaranteeing the probabilistic safety and stability. Our proposed method faces challenges when addressing unknown gain dynamics or unknown actuator gain faults. In future research, we will design an affine dot product compound kernel method to overcome these limitations.

Limited transmission bandwidth and actuator faults in intelligent and connected vehicles (ICVs) pose significant challenges to controller design. To address these issues, this paper proposes a transmission-efficient fault-tolerant control strategy for a platoon of ICVs. Adaptive laws are developed to estimate the bounds of unknown fault characteristics, which eliminates the need for prior fault knowledge. A quantized event-triggered (QET) mechanism that integrates a hysteresis quantizer and a dynamic event-triggered mechanism (DETM) is designed to improve signal transmission efficiency. By appropriately designing the dynamic variable in the DETM, the Zeno behavior is avoided. Furthermore, with the aid of smooth functions, an adaptive controller is constructed to address the impact of unknown actuator faults, as well as signal deviations induced by the QET mechanism. Simulation results in both representative and real-world driving scenarios demonstrate that the proposed strategy significantly reduces communication burden while ensuring robustness against unknown actuator faults.

A distributed 2 x 2 multiple-input and multiple-output (MIMO) joint radar and communication (JRC) system utilizing chaotic optoelectronic oscillators (OEOs) is proposed. The system employs independently oscillating chaotic OEOs to achieve MIMO radar waveform multiplexing. Two optical frequency combs (OFCs) are used as the optical source inputs for the respective OEOs. The Waveshaper selects the comb lines spacing, and time delay is introduced via the dispersion unit to form a microwave photonic notch filter. The tunable notch chaotic (TNC) signal features an adjustable number of notches and notch frequencies, which can be transmitted to the base station through optical fiber. The TNC signals are used for high-resolution radar detection, while the notch frequency bands are utilized for wireless signal transmission. The proposed system enables the sharing of both radio frequency front-end hardware and spectrum resources. Experimental results demonstrate that the system can generate chaotic signals with multiple notch frequency points within the 2-8 GHz band. The use of quasi-orthogonal chaotic signals enables MIMO transmit signal multiplexing at the same frequency, where spectral efficiency can be improved. The communication unit achieves wireless signal transmission at data rates of 180 Mbit/s using 64 quadrature amplitude modulation at the 3.5 GHz and 5.8 GHz notch frequency points. The error vector magnitudes comply with the 3GPP standard. This paper presents an effective way for addressing spectrum sharing challenges in electromagnetic environment with dense user populations, such as smart cities and intelligent transportation systems.

In order to solve the problems of low classification accuracy, poor quality of generated music, and insufficient consideration of the order and duration of notes in music coordination, this paper adopts a long short-term memory network (LSTM) and ensemble model based on the combination of timing and self-attention mechanism. The experimental model uses the LSTM network to automatically learn the important features of notes, and introduces the timing and self-attention mechanism to enhance the model's ability to pay attention to the note sequence and features, and better capture the long-distance dependencies and emotional changes in music. Compared with the traditional model, the model used in this paper is more detailed in considering the order and duration of notes, and combines emotional labels with audio data to improve the quality of music generation. The experiment is verified by the three music datasets of Lim, Rhyu and Lee. The ensemble model combined with LSTM and self-attention mechanism in this paper performs well in comprehensive evaluation scores and chord classification accuracy, which is significantly improved compared with the traditional LSTM model. The novelty lies in the better integration of the timing relationship and emotional information of the note sequence, which improves the performance of music coordination. The model in this paper achieved 43 points (out of 50 points) and 95.6% in comprehensive evaluation score and chord classification accuracy, respectively. The chord classification accuracy was significantly improved by 3.3% compared with LSTM. It also has unique advantages in model structure design and feature integration, especially in the introduction of timing and self-attention mechanisms, and the combination of emotional labels. It has achieved better results and brought new ideas and methods to the field of music generation.

The utilization of vehicle-to-vehicle (V2V) communication techniques within a vehicle platooning system (VPS) significantly enhances traffic safety and efficiency by exchanging information. However, packet loss can cause V2V communication to be unreliable, leading to the switching of communication topology and threatening the safety of the VPS. Furthermore, in practical scenarios, intelligent and connected vehicles (ICVs) inevitably suffer inherent unknown actuator faults that impose potential safety risks on a VPS. In this work, we propose a novel cyber-physical-level safe control framework that consists of an upper-level cyber plane and a lower-level physical plane. Within the cyber plane, we use a continuous-time Markov chain to model switching communication topology and design a fully distributed adaptive observer for each following vehicle to obtain the leader's state. Based on adaptive fault-tolerant controllers in the physical plane, each ICV can track its observer state and compensate for the influences of unknown actuator faults. Moreover, several sufficient conditions are derived to guarantee the mean square stability of the VPS and achieve the desired control objectives. Finally, simulation results are provided to verify the effectiveness of our proposed control method and to demonstrate its competitiveness.

Fault detection is not only a useful approach to guarantee the safety of a vehicle platooning system but also an indispensable part of functional safety for future connected automated vehicle development. This paper mainly concentrates on the network-based fault detection problem of a vehicle platoon with undirected topologies under a periodic event-triggered strategy (PETS). Firstly, we present a periodic event-triggered fault detection filter to generate a residual signal for this vehicle platoon subject to actuator faults and external disturbances, where PETS is employed to reduce bandwidth utilization and save communication resources. Secondly, by using the network-based fault detection filter, the vehicle platooning system, and a fault weighting system, a residual system is developed to formulate the design of the fault detection filter problem as an $H_{\infty }$ problem. Thirdly, based on Lyapunov-Krasovskii functionals, sufficient conditions are established to ensure that the residual system fulfills asymptotic stability with $H_\infty$ performance, together with a threshold is also designed for each vehicle to judge whether the fault happens or not. Finally, numerical examples and field experiments are conducted to verify our findings.

Unexpected faults occurring in a connected vehicle (CV) are inevitable, imposing potential safety risks upon a vehicle platoon, and it is more challenging to tackle in the case of heterogeneous CVs. In this paper, we propose a novel hierarchical control framework that integrates an upper observer layer and a lower tracking layer. Based on distributed fixed-time observers in the upper layer, the following vehicles can observe the leader's state through vehicle-to-vehicle (V2V) communication. Adaptive fault-tolerant control techniques are leveraged to address the issues of unknown actuator faults and help CVs track their observed leader's state in the lower tracking layer. Furthermore, we formulate two sufficient conditions to ensure the stability of closed-loop systems and the feasibility of the proposed control framework. To validate our method, three numerical examples and comparative results are provided to show the effectiveness and competitiveness of the presented techniques.

In this article, we, respectively, take the synchronization into consideration for directed and undirected complex networks (CNs) with multiple state or delayed state couplings subject to recoverable attacks. By selecting appropriate Lyapunov functional, employing inequality techniques, and adopting the designed state-feedback controller, two criteria of the synchronization are established for the directed CN with multiple state couplings (CNMSCs). Moreover, the synchronization of CNMSCs is also discussed for the case that the network topology is undirected. In addition, two types of CNs with multiple delayed state couplings are also proposed, and several criteria of synchronization are formulated for these networks. Finally, two examples are given to verify the correctness of the derived synchronization criteria.

This paper mainly attempts to discuss lag H∞ synchronization in multiple state or derivative coupled reaction–diffusion neural networks without and with parameter uncertainties. Firstly, we respectively propose two types of reaction–diffusion neural networks with multiple state and derivative couplings subject to parameter uncertainties. Secondly, by exploiting designed state feedback controllers, several criteria of the lag H∞ synchronization for these two networks are developed based on Lyapunov functional and inequality techniques. Thirdly, lag H∞ synchronization issues of these two networks are also coped with by virtue of devised adaptive control strategies. Finally, we provide two numerical examples to verify the obtained lag H∞ synchronization criteria.

This paper concentrates on the research of topology identification problem for coupled neural networks with multiple state couplings (CNNMSCs) or multiple delay state couplings (CNNMDSCs), in which single neural network may or may not include time delay. By constructing the response networks, designing suitable controllers and parameter adjustment schemes, utilizing several inequality techniques and significant lemmas, the topology of the original networks are identified based on synchronization between the original networks and the response networks. Finally, the validity of the proposed controllers and parameter adjustment strategies is demonstrated by two numerical examples.

This paper mainly concentrates on the finite-time passivity (FTP) and finite-time synchronization (FTS) of complex networks with multiple weights (CNMWs). First, several FTP criteria of CNMWs are given through utilizing Lyapunov functional as well as a proportional-derivative (PD) controller. Then, the FTS problem for such networks is also coped with based on the FTP and PD control approach. Finally, a numerical simulation is given to illustrate our results.

In this paper, a class of multiple derivative coupled reaction-diffusion neural networks with and without parameter uncertainties is investigated. Firstly, we analyze the passivity and synchronization of the proposed network models and derive several criteria based on inequality techniques. Furthermore, a pinning control strategy is also developed to ensure that the proposed networks can achieve passivity and synchronization. Finally, a numerical example is presented to verify the effectiveness of the obtained criteria.

In this paper, two types of coupled reaction-diffusion neural networks with multiple state couplings or spatial diffusion couplings are presented. By selecting appropriate adaptive control schemes and employing inequality techniques, several passivity conditions for these network models are given. In addition, two sufficient conditions for ensuring the synchronization of the proposed network models are also established by exploiting the output-strictly passivity. Finally, we give two numerical examples to verify the effectiveness of the derived criteria.

In this paper, a coupled reaction-diffusion neural networks with multiple state couplings is presented. By selecting appropriate adaptive control scheme and employing inequality techniques, several passivity conditions for the proposed network model are given. In addition, a sufficient condition for ensuring the synchronization of the proposed network model is also established by exploiting the output-strictly passivity. Finally, we give a numerical example to verify the effectiveness of the derived criteria.

通过构建基于自通风管网的生物滤床和潜流式人工湿地中试耦合系统,对比了混合过滤石材的原有系统a和采用不同填料的对比系统b的污染物去除效果.结果表明,系统b对COD、NH;-N、TN和TP的去除率分别为74.10％、94.14％、73.57％和69.53％,优于系统a;填料的优化配置对中试耦合系统污染物去除效率的提升效果显著,系统b对上述指标的去除较原有系统a分别提升了1 1.00个、11.55个、2.69个和8.09个百分点;生物滤床单元对NH4+-N的去除占主要作用,而潜流式人工湿地单元则对去除COD、TN和TP的贡献较高.温度升高,COD去除率总体呈下降趋势,NH4+-N的去除率呈上升趋势,对TN、TP的去除效果影响不明显.