Leveraging large language models (LLMs) in traffic signal control (TSC) improves optimization efficiency and interpretability compared to traditional reinforcement learning (RL) methods. However, existing LLM-based approaches are limited by fixed time signal durations and are prone to hallucination errors, while RL methods lack robustness in signal timing decisions and suffer from poor generalization. To address these challenges, this paper proposes HeraldLight, a dual LLMs architecture enhanced by Herald guided prompts. The Herald Module extracts contextual information and forecasts queue lengths for each traffic phase based on real-time conditions. The first LLM, LLM-Agent, uses these forecasts to make fine grained traffic signal control, while the second LLM, LLM-Critic, refines LLM-Agent's outputs, correcting errors and hallucinations. These refined outputs are used for score-based fine-tuning to improve accuracy and robustness. Simulation experiments using CityFlow on real world datasets covering 224 intersections in Jinan (12), Hangzhou (16), and New York (196) demonstrate that HeraldLight outperforms state of the art baselines, achieving a 20.03% reduction in average travel time across all scenarios and a 10.74% reduction in average queue length on the Jinan and Hangzhou scenarios. The source code is available on GitHub: https://github.com/BUPT-ANTlab/HeraldLight.

Autonomous driving is a high-performance, safety-critical task. Effectively controlling autonomous vehicles to enhance both performance and safety is crucial, especially in complex and dynamic environments. However, in real-time obstacle avoidance (OA) scenarios, the planning layer often fails due to high computational complexity and response delays. This trade-off between computational efficiency and safety performance presents a key challenge: how to achieve an optimal balance between autonomous driving safety and real-time performance. In recent years, addressing OA at the control layer has become a major research focus for improving the safety of autonomous vehicles. Given the advantages of Model Predictive Control (MPC) in prediction and constraint handling, this paper integrates an OA safety distance constraint into MPC to effectively handle OA in Unmanned Ground Vehicles (UGVs). First, a Taylor expansion is used to construct the UGV’s error model. Then, safe distance constraints for obstacle avoidance are formulated, considering both tracking errors and proximity to obstacles. Additionally, a Safe Obstacle Avoidance MPC (SOAMPC) is developed by integrating safety distance constraints and physical limitations. Furthermore, key control-theoretic properties are established, including recursive feasibility, guaranteed collision avoidance, and system stability. Simulations and experiments in a multi-obstacle environment validate SOAMPC’s effectiveness. Results show that SOAMPC not only ensures obstacle avoidance and stability but also outperforms other methods in efficiency and path tracking accuracy.

LiDAR-based localization serves as a critical component in autonomous systems, yet existing approaches face persistent challenges in balancing repeatability, accuracy, and environmental adaptability. Traditional point cloud registration methods relying solely on offline maps often exhibit limited robustness against long-term environmental changes, leading to localization drift and reliability degradation in dynamic real-world scenarios. To address these challenges, this paper proposes DuLoc, a robust and accurate localization method that tightly couples LiDAR-inertial odometry with offline map-based localization, incorporating a constant-velocity motion model to mitigate outlier noise in real-world scenarios. Specifically, we develop a LiDAR-based localization framework that seamlessly integrates a prior global map with dynamic real-time local maps, enabling robust localization in unbounded and changing environments. Extensive real-world experiments in ultra unbounded port that involve 2,856 hours of operational data across 32 Intelligent Guided Vehicles (IGVs) are conducted and reported in this study. The results attained demonstrate that our system outperforms other state-of-the-art LiDAR localization systems in large-scale changing outdoor environments.

For various typical cases and situations where the formulation results in an optimal control problem, the linear quadratic regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the algebraic Riccati equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.

This article investigates the design of a robust fixed-order controller for single-input–single-output (SISO) polytopic systems with interval uncertainties, with the aim that the closed-loop stability is appropriately ensured and the performance specifications on sensitivity shaping are conformed in a specific finite frequency range. Utilizing the notion of generalized common Lyapunov strictly positive realness (CL-SPRness), the equivalence between strictly positive realness (SPRness) and strictly bounded realness (SBRness) is established; and then, the specifications on robust stability and performance are transformed into the SPRness of newly constructed systems and further characterized in the framework of linear matrix inequality (LMI) conditions. The proposed methodology avoids the tedious yet mandatory evaluations of the specifications on all vertices of the uncertain polytopic system in an explicit form. Instead, solving five LMIs exclusively suffices for ensuring the robust stability and performance regardless of the number of vertices, and thus, the typically heavy computational burden is considerably alleviated. It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore, significantly less conservatism is attained in the system performance.

Universal grasping for a diverse range of objects is a challenging problem in robotics, especially in the presence of mixed properties with fragile/rigid and heavy/light. Toward universal grasping, this article presents a practical and systematic grasping control framework that enables a variable stiffness gripper to handle the objects with diverse properties using a category-aware force regulation approach, termed classification-based force grasping. Under this framework, a convolutional neural network (CNN) is employed to classify the category of the grasping object, and a grasping force is determined based on the classified category through a database that records a predefined force magnitude per category. Sequentially, the gripper can be adjusted to a force-optimized stiffness, which facilitates the achievement of an accurate grasping force regulation in a large range. Technically, two novel enabling modules are developed for grasping classification and execution, respectively. First, a novel weight imprinting technique based on center-guided feature embedding is proposed for object classification. It enables the CNN to efficiently handle novel object categories using only a few samples even without retraining/fine-tuning. Second, a vision-based grasping force sensing module is developed, which takes advantage of the specifically designed variable-stiffness gripper. Its grasping force can be estimated from the deflection angle of finger flexure by the vision so that the contact force can be sensed and regulated. Remarkably, only single-source vision information is needed for both of the above modules without any additional force sensor. Experiments are conducted extensively to evaluate the performance of the proposed force grasping approach.Note to Practitioners—Robotic grasping often needs to handle novel categories of objects. As a result, frequent retraining of the classification neural network is a pain point, which is tedious and prone to overfitting with only a few samples. In this work, metric learning is introduced for grasping classification where a novel kind of weight imprinting classification is proposed to handle the novel classes by better feature embedding and directly setting the classifier weights without retraining or fine-tuning. Together with the benefits from the variable stiffness feature of the gripper, the proposed vision-based force grasping approach can handle a wide range of objects from fragile to heavy, and the grasping force is controllable from 0.2 N onward to the motor limitation. The controllable grasping force resolution of the proposed vision grasping is better than 0.05 N, the accuracy of the grasping force is evaluated from 0.2 to 12 N, and the evaluated grasping objects are from extremely fragile potato chips and eggshell to heavy flange and metal block.

In this work, an adaptive iterative sliding mode control method is proposed for multi-axis mechatronic systems. Commonly, the multi-axis mechatronic systems are applied in high-speed and high-precision contouring tasks. For such contouring tasks, the multi-axis coordination is a main issue. As an inevitable challenge, several factors affect the multi-axis coordinate and diminish the contouring performance. Also, some special mechanical structure brings strong coupling to the system, which makes the system identification rather difficult. To solve these problems, this work proposes a learning-based totally model-free control approach for contouring tasks in application to such multi-axis motion stages. With this approach, all the coupling, disturbance, nonlinearity, and other unknown dynamics are regarded as lumped uncertainties in each axis. As a result, these uncertainties can be attenuated and compensated by the proposed controller. To analyze the contouring performance, a case study of a flexure-linked dual-drive H-gantry system is investigated to illustrate the effectiveness of the proposed method.

A two-degree-of-freedom (2-DoF) flexure-based nanopositioner is investigated for the planar scanning tasks, and a robust controller design scheme based on the convex inner approximation method is proposed. In practice, a flexure-based mechanism is usually represented by a second-order dynamic model. However, the second-order dynamic model cannot precisely fit the real system dynamics, and the model mismatch renders it difficult to achieve satisfying system performance in applications. Such a mismatch includes the parameter uncertainties caused by inaccurate model identification, different motion conditions, as well as high-order resonances. Note that if the controller is not well designed, the high-order resonances can be frequently activated, especially when the system input variation is significant. Therefore, to deal with the above impediments, a novel scheme for the robust controller design is proposed, with the variation of system input considered. In the proposed scheme, a subset of gains that can stabilize the closed-loop system is characterized elegantly via an inner approximation method considering the model uncertainties, and the formulated optimization problem regarding the determination of the controller parameters can be efficiently solved. Furthermore, the proposed scheme guarantees the performance regarding the H2-norm level and limits the H∞-norm level in a designated range. Finally, numerical optimization and comparative experiments are carried out, and the results evidently show the effectiveness of the proposed method.

Redactable blockchain is an emerging topic in recent blockchain research. Its purpose is to delete or correct errors and harmful content on the chain without using hard forks. In other words, while preserving the public immutability of the blockchain, to allow some administrators or trusted centers with higher authority to modify the content on the blockchain without violating the integrity of the entire blockchain. In this paper, we proposed a redactable blockchain scheme based on unforgeable signatures. Compared with the existing blockchain editing solutions, this paper's solution can be efficiently and conveniently integrated into the current blockchain technology. This scheme's security is based on the collision-resistant hash function used by the original immutable blockchain and the unforgeability of the signature scheme. Besides, our solution can quickly confirm whether a chain has correctly implemented modifications issued by the central authority through the merge verification of signatures.

Dynamic control for robotic automation tasks is traditionally designed and optimized with a model-based approach, and the performance relies heavily upon accurate system modeling. However, modeling the true dynamics of increasingly complex robotic systems is an extremely challenging task and it often renders the automation system to operate in a non-optimal condition. Notably, many industrial robotic applications involve repetitive motions and constantly generate a large amount of motion data under the non-optimal condition. These motion data contain rich information, and therefore an intelligent automation system should be able to learn from these non-optimal motion data to drive the system to operate optimally in a data-driven manner. In this paper, we propose a learning-based controller optimization algorithm for repetitive robotic tasks. To achieve this, a multi-objective cost function is designed to take into consideration both the trajectory tracking accuracy and smoothness, and then a data-driven approach is developed to estimate the gradient and Hessian based on the motion data for optimization without relying on the dynamic model. Experiments based on a magnetically-levitated nanopositioning system are conducted to demonstrate the effectiveness and practical appeals of the proposed algorithm in repetitive robotic automation tasks.

Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e.g., in grasping detection, the inference skill learned from a grasping dataset is not universal enough to directly apply on various other daily/industrial applications. This paper presents an approach enabling the easy domain adaptation through a novel grasping detection network with confidence-driven semi-supervised learning, where these two components deeply interact with each other. The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher. This approach largely prevents the student model to learn the incorrect/harmful information from the consistency loss, which speeds up the learning progress and improves the model accuracy. Our results show that the proposed network can achieve high success rate on the Cornell grasping dataset, and for domain adaptation with very limited data, the confidence- driven mean teacher outperforms the original mean teacher and direct training by more than 10% in evaluation loss especially for avoiding the overfitting and model diverging.

Data-driven control methods for modern controller design are becoming popular recently. However, the traditional Proportional-Integral-Derivative (PID) controller is still the most widely used controller to the industrial preference. To tune the parameters of the PID controller, optimal PID tuning approaches such as solving the Riccati equation of the Linear Quadratic Regulator (LQR) provide the optimal solution. The disadvantages of the LQR are that an accurate model of the system is required, and the high-order system must be reduced to the second-order system so that the Riccati equation can be solved. In this paper, a novel data-driven method is proposed to cope with these problems. For the system which is difficult to be identified accurately, the proposed data-driven method can skip the procedure of system identification and tune the parameters of the PID controller directly with the experimental data instead of solving the Riccati equation. This data-driven tuning method also ensures that the parameters of the PID controller for the high-order system are optimized without using the reduced-order model of the system. Simulations are conducted on a tray indexing system with the second-order model and the full-order model demonstrating high applicability and accuracy of the proposed method.

The disturbance observer (DOB) is widely used in high precision motion control applications as an effective means of rejecting disturbances. DOB design essentially boils down to the design of the Q filter. In traditional DOB design, the Q filter follows a standard form such as the Butterworth or binomial, where the bandwidth is the only tunable parameter. In this paper, we employ a more general form of Q filter, and tune the parameters iteratively using a data-based approach according to some design criterion. Simulation results show that the data-based design achieve improved disturbance rejection in low frequency region while the high frequency noise attenuation and robust stability is not compromised.

Valve Stiction is a common nonlinear phenomenon in pneumatic control valves and it causes oscillations in the control loops. A model of valve stiction that is easy to implement and accurate is desired for analysis of this phenomenon. Compared with the physical model, the data-driven model does not require excess knowledge on various physical parameters, thus it is widely used in modeling and diagnosis of valve stiction behavior. In this paper, modifications are made to the Two-layer binary tree data-driven model to overcome its shortcomings on handling instantaneous input command on reverse motion. It has simpler logic structure compared with recent proposed XCH model. Accuracy of the revised binary tree model is then tested and validated by ISA control valve standard test.

Because the network connection information contains nominal and linear attributes, and linear attributes are divided into continuous and discrete attributes, the network connection information is the heterogeneous data. The heterogeneous distance functions are used to cluster data in this paper. The cooperative network intrusion detection based on semi-supervised clustering algorithm is proposed. Firstly, the network data flows are divided into three data flows (TCP flow, UDP flow, and ICMP flow) according to network protocol and are sent to three detection agents. Then every detection agent constructs the detection model using the fuzzy c-means clustering algorithm based on the HVDM (Heterogeneous Value Difference Metric) distance. Finally, revise and verify the detection model by using test data. Simulation experiments are done by using KDD CUP 1999 data set, results show that the method presented here is feasible and efficient.