http://bura.brunel.ac.uk/handle/2438/8631 Sat, 13 Jun 2026 12:33:07 GMT 2026-06-13T12:33:07Z http://bura.brunel.ac.uk/handle/2438/33412 Title: Quasi-Consensus Control of Delayed Multi-Agent Systems With Stochastic Communication Protocols and Amplify-and-Forward Relays Authors: Ban, J; Guo, L; Wang, Z; Cai, H; Li, B Abstract: In this paper, the observer-based quasi-consensus control problem is investigated for a class of discrete-time multi-agent systems subject to time-varying delay. To improve communication quality and extend transmission distance, a stochastic communication protocol and an amplify-and-forward relay mechanism are incorporated. During the process of signal amplification and transmission in the AaF relay, stochastic packet loss is considered, which introduces additional complexity into the system analysis. The main objective is to design a distributed observer-based control strategy capable of handling time-varying delays, stochastic scheduling governed by a Markov chain, and random packet dropouts. Sufficient conditions are derived to guarantee the achievement of quasi-consensus in probability among the agents. These conditions are formulated in terms of matrix inequalities through which the required gain matrices are computed. A simulation example is provided to validate the effectiveness and robustness of the proposed control approach under realistic communication constraints. Fri, 22 May 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33412 2026-05-22T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33406 Title: Robust Model Predictive Control for Polytopic Uncertain Systems With Energy Harvesting Sensors Under Round-Robin Protocol Authors: Cai, H; Wang, Z; Song, Y; Li, P Abstract: This paper addresses the robust model predictive control problem for a class of networked control systems with polytopic uncertainties and hard constraints, where the controller design is complicated by the joint presence of an energy harvesting sensor in the forward channel and the round-robin protocol in the backward channel. In such a setting, stochastic transmission behavior caused by random energy availability, together with fixed communication scheduling and immeasurable states, makes it difficult to guarantee recursive feasibility of the online optimization and mean-square stability of the closed-loop system. To capture these features, the mathematical expectation of a quadratic function depending on both the sensor energy level and the transmission order over an infinite horizon is constructed to formulate the optimization problem. In order to cope with the terminal constraint set and the immeasurability of system states, an auxiliary optimization problem with guaranteed solvability is developed by employing inequality analysis and slack-matrix techniques, through which a sub-optimal solution is obtained. Furthermore, sufficient conditions are derived to ensure the recursive feasibility of the proposed algorithm and the mean-square stability of the resulting closed-loop system with and without hard constraints. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method. Tue, 05 May 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33406 2026-05-05T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33405 Title: Collaboration Better Than Integration: A Novel Time-Frequency-Assisted Deep Feature Enhancement Mechanism for Few-Shot Transfer Learning in Anomaly Detection Authors: Mao, W; Wu, J; Du, S; Feng, K; Wang, Z Abstract: Deep transfer learning has achieved significant success in anomaly detection over the past decade, but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks. To address this issue, a novel time-frequency-assisted deep feature enhancement (TFE) mechanism is proposed. Unlike traditional methods that integrate time-frequency analysis with deep neural networks, TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space, where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations: 1) Enhancement, where a frequency-importance-driven contrastive learning (FICL) network transfers physically-aware information from wavelet scattering features to deep features, and 2) Feedback, which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance. TFE is applied to a domain-adversarial anomaly detection framework and, through alternating training, significantly enhances both deep feature discriminative power and few-shot anomaly detection. Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error. Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning. Thus, collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection. Tue, 10 Mar 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33405 2026-03-10T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33404 Title: Zonotopic Set-Membership Fusion Estimation for Multi-Sensor Systems Under FlexRay Protocol Authors: Zhao, Z; Wang, Z; Liang, J Abstract: Networked multi-sensor systems operating under the FlexRay protocol (FRP) are widely used in automotive industry, where reliable state estimation under bounded uncertainties is of fundamental importance. In such systems, the measurement information from multiple sensors is transmitted to the estimator through a network governed by the FRP, which induces scheduling constraints and switching behaviors in the estimation process. These characteristics make it challenging to guarantee accuracy and boundedness of the state estimates using conventional methods. This paper investigates the zonotopic set-membership fusion estimation (SMFE) problem for multi-sensor systems under the FRP. The research objective is to design a parallel fusion estimation algorithm for the transformed switched system, to establish a sufficient condition guaranteeing the ultimate boundedness of the radii of the resulting zonotopes, and to improve the transient estimation performance. An SMFE algorithm is proposed to recursively calculate the zonotopes that constrain the system state by exploiting the properties of zonotopes. A sufficient condition is derived to ensure the ultimate boundedness of the output zonotopes’ radii, which explicitly takes into account both the scheduling of the FRP and the adverse effect of zonotope order reduction on estimation performance. Furthermore, a matrix-inequality-based method is developed to construct an additional enclosing zonotope, based on which a tighter zonotope is obtained at each time instant to enhance the transient performance. The efficacy of the proposed SMFE method is demonstrated through two simulation experiments. Wed, 25 Mar 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33404 2026-03-25T00:00:00Z