Zhixin Sun

In this paper, a novel optimal control scheme for path tracking of autonomous ground vehicles is proposed based on the adaptive dynamic programming algorithm. Unlike existing modeling methods, this paper uses generalized fuzzy hyperbolic models (GFHM) to approximate the model uncertainties induced by the actual environmental disturbances. The optimal tracking control problem is formulated as solving the Hamilton-Jacobi-Isaacs (HJI) equation, which involves the nonlinearity of the model. Meanwhile, a dynamic memory event-triggered mechanism (DMETM) is introduced, whose triggering threshold is capable of dynamic adjustment according to the historical system states. The uniform ultimate boundedness (UUB) stability of the designed control system is rigorously proved. Furthermore, a reinforcement learning algorithm is used to solve the constructed HJI equation and address the path tracking control problem. Finally, the effectiveness of the proposed algorithm is verified through theoretical analysis and numerical simulations.

This article proposes a fault-tolerant control strategy for uncrewed marine vehicles (UMVs) suffering from unknown actuator bias faults to avoid the structural complexity of a conventional Takagi–Sugeno (T–S) fuzzy model. A fault observer is introduced to estimate the actuator fault through online learning for real-time control input compensation. Furthermore, to conserve network resources, an adaptive frequency event-triggered mechanism (AFETM) is applied. Unlike conventional event-triggered control methods that use fixed thresholds, AFETM dynamically adjusts the triggering condition based on historical transmission frequency and error, effectively reducing unnecessary data transmission. Moreover, instead of the traditional dual-network adaptive dynamic programming (ADP) structure, a single-critic network is employed to solve the Hamilton–Jacobi–Isaacs (HJI) equation, significantly reducing computational burden and parameter complexity. In addition, the stability of the UMV system is analyzed using the Lyapunov stability theorem, which demonstrates that the system states are uniformly ultimately bounded (UUB) under the proposed method. Simulation results demonstrate that the proposed AFETM reduces communication load by 99.6% and 25.4% compared with time-triggered mechanisms (TTMs), static and event-triggered methods, while ensuring UUB stability of the system states under actuator faults and disturbances.

As supply chains expand in scale and the number of participating nodes increases, existing consensus algorithms are increasingly showing limitations in scalability, communication complexity, and handling complex network environments. To address the shortcomings of blockchain consensus mechanisms in master node selection, scalability, and communication complexity in supply chain traceability scenarios, this paper proposes a blockchain hybrid consensus optimization algorithm named Node Rating-Based and Grouping Raft cluster Practical Byzantine Fault Tolerance (NG-RPBFT) for supply chain traceability. This algorithm builds a multi-index comprehensive rating model for nodes to comprehensively evaluate consensus nodes, reasonably groups consensus nodes, adopts an inter-group and intra-group dual consensus mechanism to achieve efficient data synchronization, and introduces Brotli data compression technology to optimize message load, effectively enhancing system performance. Experimental results confirm that this algorithm significantly improves the scalability of the consensus mechanism and performs exceptionally well in consensus efficiency, making it suitable for complex application scenarios such as supply chain traceability under CPS scenarios.

A central obstacle to the practical deployment of Reinforcement Learning (RL) is the prevalence of sparse rewards, which often necessitates task-specific dense signals crafted through costly trial-and-error. Automated reward decomposition and return–redistribution methods can reduce this burden, but they are largely semantically agnostic and may fail to capture the multifaceted nature of task performance, leading to reward hacking or stalled exploration. Recent work uses Large Language Models (LLMs) to generate reward functions from high-level task descriptions, but these specifications are typically static and may encode biases or inaccuracies from the pretrained model, resulting in a priori reward misspecification. To address this, we propose the Metacognitive Introspective Reward Architecture (MIRA), a closed-loop architecture that treats LLM-generated reward code as a dynamic object refined through empirical feedback. An LLM first produces a set of computable reward factors. A dual-loop design then decouples policy learning from reward revision: an inner loop jointly trains the agent’s policy and a reward-synthesis network to align with sparse ground-truth outcomes, while an outer loop monitors learning dynamics via diagnostic metrics and, upon detecting pathological signatures, invokes the LLM to perform targeted structural edits. Experiments on MuJoCo benchmarks show that MIRA corrects flawed initial specifications and improves asymptotic performance and sample efficiency over strong reward-design baselines.

Image clustering analysis faces the curse of dimensionality, distance concentration, multimodal landscapes, and rapid diversity loss that challenge meta-heuristics. Meanwhile, the standard Crayfish Optimization Algorithm (COA) has shown notable potential but often suffers from poor convergence speed and premature convergence. To address these issues, this paper introduces a Chaos-initiated and Adaptive Multi-guide Control-based COA (CMCOA). First, a chaotic initialization strategy is employed by explicitly exploiting the reflection symmetry of logistic-map chaotic sequences together with opposition-based learning, which enhances population diversity and facilitates early exploration of promising regions. Second, a fitness-feedback adaptive parameter control mechanism, motivated by the general idea of the MIT rule, is integrated to dynamically balance exploration and exploitation, thereby accelerating convergence while mitigating premature stagnation. Furthermore, a multi-guide stage-switching strategy is designed to avoid being trapped in local optima by promoting adaptive transitions between exploration phases and exploitation phases. CMCOA is benchmarked against competing algorithms on ten challenging test functions drawn from CEC2017, CEC2019, CEC2020, and CEC2022 suites. We also conducted multispectral clustering, where class differences often lie in reflectance magnitude; we adopt Euclidean distance for its efficiency and suitability in capturing such variations. Compared with other algorithms, CMCOA shows faster convergence, higher accuracy, and improved robustness, revealing its broader potential for image analysis tasks.

With the rapid development of large-scale sensor networks, visual sensor networks (VSNs) have attracted significant interest from academia and industry. VSNs can transmit more information and allow for more fine-grained monitoring of objects than conventional methods. In this survey, we provide a comprehensive review on image segmentation and super-resolution reconstruction, two key image processing tasks for VSNs, since these two tasks can effectively improve the performance and balance key factors for the performance of VSNs (e.g., network bandwidth, computing resources, and sensor battery life). We begin by expounding the basic concepts and an overall framework of VSNs. Furthermore, we examine state-of-the-art approaches and provide a new taxonomy of existing research topics. Finally, we outline several challenges, possible solutions, and future research directions of these two key image processing tasks for VSNs.

Reliable short-term electricity usage prediction is essential for preserving the stability of topologically symmetric power networks and their dynamic supply–demand equilibrium. To tackle this challenge, this paper proposes a novel approach derived from the standard Extreme Learning Machine (ELM) by integrating an enhanced Crayfish Optimization Algorithm (DSYCOA). This algorithm combines Logistic chaotic mapping, local precise search, and dynamic parameter adjustment strategies designed to achieve a dynamic balance between exploration and exploitation, thereby optimizing the initial thresholds and weights of the ELM. Consequently, a new short-term power load forecasting model, namely the DSYCOA-ELM model, is developed. Experimental validation demonstrates that the improved DSYCOA exhibits fast convergence speed and high convergence accuracy, and successfully harmonizes global exploration and local exploitation capabilities while maintaining an empirical balance between exploration and exploitation. To additionally verify the effectiveness of DSYCOA in improving ELM, this paper conducts simulation comparison experiments among six models, including DSYCOA-ELM, ELM, and ELM improved by BWO (BWO-ELM). The findings demonstrate that the DSYCOA-ELM model outperforms the other five forecasting models in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other indicators. Specifically, in terms of MAPE, DSYCOA-ELM reduces the error by 96.9% compared to ELM. This model demonstrates feasibility and effectiveness in solving the problem of short-term power load prediction, providing critical support for maintaining the stability of grid topological symmetry and supply–demand balance.

The use of UAVs for logistics delivery has become a hot topic in current research, and how to plan a reasonable delivery route is the key to the problem. Therefore, this paper proposes a multi-environment logistics delivery route planning model that is based on UAVs, is characterized by a 3D environment model, and aims at the shortest delivery route with minimum flight undulation. In order to find the optimal route in various environments, a multi-strategy improved artificial lemming algorithm, which integrates the Cubic chaotic map initialization, double adaptive t-distribution perturbation, and population dynamic optimization, is proposed. The symmetric nature of the t-distribution ensures that the lemmings conduct extensive searches in both directions within the solution space, thus improving the convergence speed and preventing them from falling into local optimal solutions. Through data experiments and simulation analysis, the improved algorithm can be successfully applied to the 3D route planning model, and the route quality is superior.