Chongrong Fang

The problem of finite/fixed-time tracking control has garnered considerable interest, owing to the stringent requirement on convergence performance in engineering applications. However, existing finite/fixed-time control methods are not directly applicable to polynomial systems, since the introduced fractional power term disrupts the inherent polynomial structure. To overcome this problem, we propose an observer-based tracking control strategy that achieves finite/fixed-time convergence for polynomial systems with unmeasurable states. First, a polynomial state observer is designed to estimate unmeasurable states. Based on the estimation results, finite-time and fixed-time tracking controllers are developed. Then, by utilizing Lyapunov stability theory and sum of squares optimization technique, sufficient conditions are constructed for the practical finite/fixed-time stability of the closed-loop system, enabling the co-design of observer and controller gains. Moreover, settling times are explicitly derived, and the fixed-time convergence is independent of initial system states. Finally, our theoretical methods are verified by simulation results.

Security issues are of significant importance for cyber-physical systems (CPS), where the attack design is a major concern. Most related studies on attack design implicitly assume that the control and detection periods are the same. However, the two periods could be different in practical systems with remote detection such as supervisory control and data acquisition (SCADA) systems, which could lead to new vulnerabilities for attackers. In this paper, we consider the design of innovation-based stealthy linear attack strategies for CPS when the control period and detection period are inconsistent. Specifically, we propose an attack framework that consists of attack strategies for detection and non-detection instants under the period discrepancy. On this basis, we design the optimal stealthy innovation-based linear attack strategies for state estimation and Linear Quadratic Gaussian (LQG) control to maximize the estimation error and control cost, respectively. We then prove that the estimation error and controller cost caused by the attacker are bounded. We further propose a countermeasure combining a secure module based on the pseudo-random number (PRN) technique with the multivariate exponentially weighted moving average (MEWMA) detector to help detect this type of attack. Simulations are given to demonstrate the effectiveness of the proposed attack strategies and the countermeasure.

This paper introduces an underwater multi-robot platform, named Aucamp, characterized by cost-effective monocular-camera-based sensing, distributed protocol and robust orientation control for localization. We utilize the clarity feature to measure the distance, present the monocular imaging model, and estimate the position of the target object. We achieve global positioning in our platform by designing a distributed update protocol. The distributed algorithm enables the perception process to simultaneously cover a broader range, and greatly improves the accuracy and robustness of the positioning. Moreover, the explicit dynamics model of the robot in our platform is obtained, based on which, we propose a robust orientation control framework. The control system ensures that the platform maintains a balanced posture for each robot, thereby ensuring the stability of the localization system. The platform can swiftly recover from an forced unstable state to a stable horizontal posture. Additionally, we conduct extensive experiments and application scenarios to evaluate the performance of our platform. The proposed new platform may provide support for extensive marine exploration by underwater sensor networks.

TCP ensures reliable transmission through its stateful implementation and remains crucial today. TCP performance profiling is essential for tasks like diagnosing network performance problems, optimizing transmission performance, and developing new TCP variants, etc. Existing profiling methods lack enough attention to TCP state transition to provide detailed insights on TCP performance. Thus, we build TcpSight, a tool focusing on TCP state transition throughout connection lifetimes. TcpSight conducts stateful analysis by capturing key events using an efficient per-connection lock-free data management mechanism. Besides, TcpSight enhances profiling by integrating application layer information collected from the TCP stack. With the profiling results, users can identify the culprit of TCP performance degradation, and evaluate the performance of TCP algorithms. We design optional modules and filtering mechanisms to reduce TcpSights overhead. Our evaluation presents that TcpSight incurs an additional CPU consumption of about 16.6% (without filtering) and 10.6% (with filtering) when the servers load is 55.7%, and generates storage consumption about 1.88 KB per connection on average. We also give application cases of TcpSight and the deployment experiences in Alibaba Cloud. TcpSight helps in revealing meaningful findings and insights into exploiting TCP in the production deployment.

The distributed identification of network systems under local observation has recently become one of the research hot spots. Due to the unobservable influence of other subsystems and the noise, it is difficult for local subsystems to achieve convergent identification under local observations. This paper proposes a distributed identification algorithm for local subsystems to identify their parameter matrices under local observation with a guarantee of convergence. We obtain the necessary and sufficient conditions for the convergence of local identification. We prove that by injecting an independent random signal to the local subsystem, which is easy to implement, using a simple least-squares method to identify local systems can achieve convergent identification for a certain probability. Meanwhile, we give the minimal level of variance of the injected signal that can still guarantee the convergence of the identification. Next, we add the designed input as a vector of Gaussian noises with decreasing variance to a known controller, proving that it can converge to the original controller. Simulations demonstrate the effectiveness of the proposed algorithms.

The interaction topology of multiagent systems (MASs) is crucial for effective collaboration. Recent advances in topology inference provide a better understanding of the behaviors of the systems. Nevertheless, external attackers can exploit such techniques, posing a severe privacy breach, while the challenges to the topology protection problem remain unresolved. This article proposes a distributed collaboration algorithm for MASs to defend against topology inference attacks. The novelties include: 1) Compared with traditional noise-adding methods that inject decaying random inputs, the proposed algorithm constructs a novel noise term to increase the irregularity of the agents’ states in a distributed manner while satisfying the convergence requirements. 2) A weight-selecting strategy is designed to choose the subtopologies to degrade the topology inference accuracy, further improving the topology-protecting performance. Theoretically, we derive the mean-square convergence factor and the nonasymptotic error bounds of our proposed algorithm. Extensive simulations demonstrate the effectiveness of the proposed algorithm in protecting the topology.

The predict-then-optimize (PTO) framework is indispensable for addressing practical stochastic decision-making tasks. It consists of two crucial steps: initially predicting unknown parameters of an optimization model and subsequently solving the problem based on these predictions. Elmachtoub and Grigas [1] introduced the Smart Predict-then-Optimize (SPO) loss for the framework, which gauges the decision error arising from predicted parameters, and a convex surrogate, the SPO+ loss, which incorporates the underlying structure of the optimization model. The consistency of these different loss functions is guaranteed under the assumption of i.i.d. training data. Nevertheless, various types of data are often dependent, such as power load fluctuations over time. This dependent nature can lead to diminished model performance in testing or real-world applications. Motivated to make intelligent predictions for time series data, we present an autoregressive SPO method directly targeting the optimization problem at the decision stage in this paper, where the conditions of consistency are no longer met. Therefore, we first analyze the generalization bounds of the SPO loss within our autoregressive model. Subsequently, the uniform calibration results in Liu and Grigas [2] are extended in the proposed model. Finally, we conduct experiments to empirically demonstrate the effectiveness of the SPO+ surrogate compared to the absolute loss and the least squares loss, especially when the cost vectors are determined by stationary dynamical systems and demonstrate the relationship between normalized regret and mixing coefficients.

This paper presents a heterogeneous underwater multi-robot system that synergistically combines the capabilities of Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) to improve underwater operations. The system leverages the realtime control and detailed intervention capabilities of ROVs with the extensive area coverage and autonomous exploration advantages of AUVs. A key innovation in this research is the implementation of Radio Frequency Identification (RFID) technology for efficient and low-power underwater wireless communication between two vehicles. The RFID-based system facilitates rapid data exchange, enabling the AUV to transmit collected environmental data to the ROV, which then relays the information to ground station operators. This communication framework allows for real-time decision-making and the issuance of directives to the AUV, ensuring an efficient collaboration between the two vehicle types. The paper also proposes a vehicle alignment approach to ensure reliable data transmission during RFID-based wireless communication. Through real-world experiments, the effectiveness of the proposed system is validated.