In the field of Deep reinforcement learning (DRL), enhancing exploration capabilities and improving the accuracy of Q-value estimation remain two major challenges. Recently, double-actor DRL methods have emerged as a promising class of DRL approaches, achieving substantial advancements in both exploration and Q-value estimation. However, existing double-actor DRL methods feature actors that operate independently in exploring the environment, lacking mutual learning and collaboration, which leads to suboptimal policies. To address this challenge, this work proposes a generic solution that can be seamlessly integrated into existing double-actor DRL methods by promoting mutual learning among the actors to develop improved policies. Specifically, we calculate the difference in actions output by the actors and minimize this difference as a loss during training to facilitate mutual imitation among the actors. Simultaneously, we also minimize the differences in Q-values output by the various critics as part of the loss, thereby avoiding significant discrepancies in value estimation for the imitated actions. We present two specific implementations of our method and extend these implementations beyond double-actor DRL methods to other DRL approaches to encourage broader adoption. Experimental results demonstrate that our method significantly improves twenty state-of-the-art (SOTA) DRL methods, including SOTA double-actor DRL methods, across eleven tasks, as measured by return and other metrics.

With the increasing adoption of autonomous vehicles, ensuring the reliability of autonomous driving systems (ADSs) deployed on autonomous vehicles has become a significant concern. Driving simulators have emerged as crucial platforms for testing ADSs, offering realistic, dynamic, and configurable environments. However, existing simulation-based ADS testers have largely overlooked the reliability of the simulators, potentially leading to overlooked violation scenarios and subsequent safety security risks during real-world deployment. In our investigations, we identified that collision detectors in simulators could fail to detect and report collisions in certain collision scenarios, referred to asignored collision scenarios. This paper aims to systematically discover ignored collision scenarios to improve the reliability of autonomous driving (AD) simulators. To this end, we present ICSFuzz, a black-box fuzzing approach to discover ignored collision scenarios efficiently. Drawing upon the fact that the ignored collision scenarios are a sub-type of collision scenarios, our approach starts with the determined collision scenarios. Following the guidance provided by empirically studied factors contributing to collisions, we selectively mutate arbitrary collision scenarios in a step-wise manner toward the ignored collision scenarios and effectively discover them. We compare ICSFuzz with multiple state-of-the-art simulation-based ADS testing methods, by replacing their oracle with our ignored-collision-aware oracle. The evaluation demonstrates that ICSFuzz outperforms ADS testers by finding 7~40x more ignored collision scenarios with a 10~105x speedup. Within the discovered ignored collision scenarios, there are two more types of ignored collision scenarios that ADS testers did not find. All the discovered ignored collisions have been confirmed by developers with one CVE ID assigned.

An unmanned ground swarm (UGS) system consists of multiple vehicles exhibiting intelligent and coordinated behavior through mutual cooperation and information exchange. The system is expected to simultaneously achieve four key objectives: swarm tracking, compact formation, collision avoidance, and obstacle evasion. However, these objectives are inherently conflicting—for instance, maintaining compact formation and ac curate tracking may increase the risk of inter-agent collisions or obstacle encounters. To resolve these conflicts, we propose a novel integrated control framework that unifies the four objectives into a consistent set of constraints. This framework systematically ad dresses the performance trade-offs through a robust and adaptive control scheme capable of handling dynamic agent behaviors and system uncertainties. Robustness is ensured without prior knowledge of the uncertainties, while an adaptive mechanism further reduces the control effort required. Moreover, fuzzy-set theoretic formulation is employed to quantify uncertainty bound, enabling the expression of performance indices that link control parameters to system behavior. These indices are then used in a Pareto-game framework to achieve parameter optimality. The effectiveness of the proposed control design—characterized by its robustness, adaptability, and optimality—is validated through simulations of a UGS team scenario.

To enhance the efficiency and safety of transportation, low-altitude economy has garnered increasing attention. In this study, we consider air–ground systems composed of both uncrewed ground vehicles and uncrewed aerial vehicles. A novel integrated framework for modeling, control, and optimization is proposed, which creatively unifies kinematics, dynamics, and control into a structured setting for approximate constraint following. The framework consists of three key phases. First, in the modeling phase, desired system behaviors, such as collision avoidance, compact formation, and trajectory tracking, are formulated into a unified performance metric. This metric is then treated as a constraint, and the system's dynamics for approximate constraint following are derived using the Udwadia–Kalaba approach. Second, in the control phase, an adaptive robust control scheme is developed to compensate for system uncertainties. Notably, the method does not require prior knowledge of the uncertainty bounds, while still guaranteeing consistent system performance in uncertain environments. Third, in the optimization phase, the fuzzy nature of the uncertainty bounds is considered. The uncertainty bound is modeled as a fuzzy set characterized by a membership function, which is incorporated into a fuzzy-theoretic performance index. The Pareto-optimal game theory is then employed to determine the optimal control parameters. The proposed framework enables the air–ground logistics system to follow constraints with reduced error, as validated through simulation results.

World models have gained significant attention as a promising approach for autonomous driving. By emulating human-like perception and decision-making processes, these models can predict and adapt to dynamic environments. Existing methods typically map high-dimensional observations into compact latent spaces and learn optimal policies within these latent representations. However, prior work usually jointly learns ego-vehicle dynamics and environmental transition dynamics from the image input, leading to inefficiencies and a lack of robustness to variations in vehicle dynamics. To address these issues, we propose the Vehicle Dynamics embedded Dreamer (VDD) method, which decouples the modeling of ego-vehicle dynamics from environmental transition dynamics. This separation allows the world model to generalize effectively across vehicles with diverse parameters. Additionally, we introduce two strategies to further enhance the robustness of the learned policy: Policy Adjustment during Deployment (PAD) and Policy Augmentation during Training (PAT). Comprehensive experiments in simulated environments demonstrate that the proposed model significantly improves both driving performance and robustness to variations in vehicle dynamics, outperforming existing approaches.

Accurate localization plays an important role in high-level autonomous driving systems. Conventional map matching-based localization methods solve the poses by explicitly matching map elements with sensor observations, generally sensitive to perception noise, therefore requiring costly hyperparameter tuning. In this paper, we propose an end-to-end localization neural network which directly estimates vehicle poses from surrounding images, without explicitly matching perception results with HD maps. To ensure efficiency and interpretability, a decoupled BEV neural matching-based pose solver is proposed, which estimates poses in a differentiable sampling-based matching module. Moreover, the sampling space is hugely reduced by decoupling the feature representation affected by each DoF of poses. The experimental results demonstrate that the proposed network is capable of performing decimeter level localization with mean absolute errors of 0.19m, 0.13m and 0.39° in longitudinal, lateral position and yaw angle while exhibiting a 68.8% reduction in inference memory usage.

Pedestrian trajectory prediction is significant for autonomous vehicles, but the difficulty of pedestrian trajectory prediction lies in the accurate modeling of pedestrian multiple interactions. In this paper, we attempt to explore the essential features of pedestrian interaction and propose a pedestrian trajectory prediction method based on multiple interactions. Firstly, considering that the interaction between self-driving cars and pedestrians resembles a dynamic game process involving sequential adaptation, we map them to the same feature space and design a temporal cross-attention mechanism to model the interaction between pedestrians and vehicles. Meanwhile, pedestrian-scene interaction is affected by the global environment as well as the local environment. To capture the global information while preserving the spatial location of pedestrians in the scene, we design a pedestrian-scene heatmap fusion (PSHF) framework to model the pedestrian-scene interaction features. We validate the effectiveness of our algorithm on the publicly available JAAD and PIE datasets, achieving better performance than existing representative methods in both single-trajectory and multi-trajectory prediction tasks. We conducted a thorough ablation study, cross-dataset validation, and qualitative visualization experiments, demonstrating the effectiveness and robustness of our method.

In this paper, an upper limb prosthesis has been furnished with a novel vision-based manipulation and grasping strategy. The proposed whole-body safety-critical control design includes vision servoing, multiple tasks planning with strict priorities, which can be formulated as an hierarchical multi-task optimization (HMO) problem with safety conditions—expressed as control barrier functions (CBF). Firstly, a modified YOLOv7 algorithm with key points detection is developed to determine the grasping pattern of the object and extract its edge contour information using a depth camera. An HMO-based strategy with a notion of CBF, providing inequality constraints in the control input, is proposed to handle multiple prioritized tasks with various constraints to offer guarantees of safety with the whole-body motion in consideration. Then the HMO problem is solved by a neuro-dynamics optimization solution online. Finally, experiments are implemented by using a self-developed upper limb prosthesis. Experimental results validate the performance of the proposed whole-body control strategy. Note to Practitioners—The intuitive, convenient and autonomous control of prosthetics has always been the object of researchers’ efforts. This paper proposes a whole-body safety-critical control design, including artificial perception system, autonomous control system under visual guidance, and user volition control system. The HMO strategy with safety conditions-expressed as control barrier functions in the context of real-time optimization-based method can fully consider the task requirements of different priorities and realize the orderly execution of prosthetic tasks. The vision feedback can detect surrounding environments in real time to obtain fine position information and optimal grasping pattern, which are conducive to reduce the amputees’ cognitive burden and provide new ideas for the whole body control of prostheses.

This paper addresses the performance regulation of Unmanned Ground Vehicle (UGV) swarm systems, where multiple vehicles travel together to complete a task. The system must satisfy two key performance requirements: maintaining vehicle coalition and avoiding collisions. These requirements are inherently conflicting, as staying close together increases the risk of collisions. Additionally, the system must contend with modeling uncertainties and disturbances. We propose a novel differential homeomorphic approach, introducing a$\beta _{i}$-performance measure that harmoniously combines these conflicting requirements. Our approach ensures that vehicles remain close to each other without collisions, even under uncertainty. To achieve this, we develop an adaptive robust control scheme that guarantees desirable$\beta _{i}$-performance. Furthermore, we optimize the control design parameters using a cooperative game-theoretic framework, establishing the existence, uniqueness, and closed-form solutions of Pareto optimality. Consequently, our approach meets four critical performance criteria for UGV swarm systems: coalition, collision avoidance, robustness, and optimality.

Path tracking for autonomous vehicles is one of the most critical tasks in intelligent transportation systems (ITS). The ITS performance, including efficiency, safety, flexibility, and resilience, are all based on it. The two central issues for a successful path tracking are resilience and smoothness. We endeavor to adopt a neural-activation based constraint-following approach to resolve these two issues concurrently. First, an adaptive robust constraint-following control scheme is proposed. The control tracks a desired trajectory with guaranteed performance even in the presence of uncertainty. Second, a neural-activation mechanism is proposed, which generates desired trajectory effectively based on traffic pattern with sufficiently smoothness. Third, the trajectory is embedded into the control scheme to ensure that the control conforms to any changing traffic pattern while in motion. As a result, the control can rapidly adapt to the changing traffic condition with smoothness and resilience.

Accurate tracking control of autonomous cooperative transportation systems (CTS) remains challenging owing to the complexity of the mechanisms and the high requirements of coordination between carriers. In this paper, the trajectory tracking control of a CTS with a pair of autonomous vehicles serving as carriers is investigated. A novel constraint-oriented hierarchical modeling method is proposed to describe the dynamics of the system. By dividing the system dynamics into two portions: the lower-level individual modeling and the upper-level constraints abstraction, the modeling process is significantly simplified. Then an innovative constraint-following control law is designed to address the tracking control problem under the special system topology, based on the internal and external constraints designed in the modeling process. The asymptotic convergence of the tracking error is theoretically guaranteed. To reduce potential damage of the payload during transportation, a payload force optimization method is creatively proposed. It relies on the closed-form relationship between the control input and payload forces established by the constraint-oriented modeling. The normal and shear stress on the payload is successfully limited, without affecting the trajectory tracking performance. Simulation results show that the proposed control method and the payload force optimization strategy can help achieve accurate and safe autonomous cooperative transportation simultaneously.

In this paper, the dynamic modeling and collaborative control of a multi-vehicle cooperative transportation system for load carrying is explored. A hierarchical modeling and constraint-following control scheme is creatively proposed. In the dynamic modeling stage, the separate models of system components including the load and vehicle carriers are firstly established at the lower level. Then the internal and external constraints corresponding to the system topology and the transportation task are designed to integrate separate models at a higher level. In the system control stage, a distributed collaborative control law is proposed based on the closed-form constraint forces, with which the load can follow the external constraints actively and the carriers can maintain the internal constraints passively. In order to overcome the influence of time-varying multi-source uncertainties of the system on control effectiveness and stability, an adaptive robust control term is designed based on the Lyapunov min-max approach. Both uniform boundedness and uniform ultimate boundedness of the constraint-following error are guaranteed. Comprehensive validations show that our propose scheme can significantly reduce the modeling complexity despite the strongly coupled topology and nonlinearity of the system, as well as achieving more precise and robust trajectory following control compared with the baseline methods.

In this article, the trajectory generation and motion control of autonomous driving oversized cargo cooperative transportation systems (CTS) in static but bounded environment is investigated. Different from common vehicle systems, the challenges lie on the safety-guaranteed cooperation of independently controlled carriers with inherent connections brought by the rigid payload, which results in complex system dynamics and multiple time-variant uncertainties. A constraint-oriented “leader-follower” modeling and control framework is introduced, and a trajectory generation method based on the diffeomorphism is creatively proposed to generate closed-form collision-free trajectory for the payload in the bounded environment. To achieve safety-guaranteed trajectory following under uncertainties, a transformed adaptive robust control strategy (TARC) is designed through constraint relaxation, and the coordination of the carriers is realized. An implementation with comprehensive ablation studies demonstrates the effectiveness of our trajectory generation and tracking control framework. The collision-free trajectory set is efficiently generated, and the CTS can be kept strictly inside the safe corridor with high tracking accuracy, which is extremely hard for the baseline methods.

Ensuring the safety of pedestrians is essential and challenging when autonomous vehicles are involved. Classical pedestrian avoidance strategies cannot handle uncertainty, and learning-based methods lack performance guarantees. In this paper we propose a hybrid reinforcement learning (HRL) approach for autonomous vehicles to safely interact with pedestrians behaving uncertainly. The method integrates the rule-based strategy and reinforcement learning strategy. The confidence of both strategies is evaluated using the data recorded in the training process. Then we design an activation function to select the final policy with higher confidence. In this way, we can guarantee that the final policy performance is not worse than that of the rule-based policy. To demonstrate the effectiveness of the proposed method, we validate it in simulation using an accelerated testing technique to generate stochastic pedestrians. The results indicate that it increases the success rate for pedestrian avoidance to 98.8%, compared with 94.4% of the baseline method.

The integration and interaction of proprioception and exteroception in the human multisensory network facilitate high-level cognitive functionalities, such as cross-modal integration, recognition, and imagination for accurate evaluation and comprehensive understanding of the multimodal world. In this article, we propose a novel cross-modal integration framework for the upper limb prosthesis based on type-2 fuzzy logic system (FLS), which can facilitate the high-level cognitive and dexterous manipulation of the prosthesis by combing human's surface electromyography (sEMG) with the computer vision. First, a transfer learning approach is proposed to improve the decoding of human's intent and enhance the effectiveness of skill transition. Then the sEMG signals and image information are integrated to jointly determine the grasp posture of the bionic hand based on fuzzy decision strategy. Fusing multisensory data and using cross-modal integration, the system is capable of crossmodally recognizing multimodal information. In order to realize the prosthesis automatically reaching the target position under the guidance of computer vision, an interval type-2 fuzzy logic controller considering uncertain dynamic parameters and disturbance is designed. Experiments are performed in some typical 3C assembly scenarios, and results show that our proposed strategy can obviously improve the accuracy of grasp posture selection and trajectory tracking effect, which brings more potential job opportunities with hope to amputees, and provides a promising approach toward robotic sensing and perception.

The existing hand gesture classification research based on surface electromyogram (sEMG) faces the challenges of low classification accuracy, weak real-time ability, weak robustness, few categories, and lack of explainability. In this article, we investigate how to classify sEMG signals for grasp recognition and human–robot interaction to consider these issues. A novel interval type-2 (IT2) fuzzy classifier based on explainable neural network is proposed for sEMG gesture recognition. Based on fully connected neural network, the adaptive moment estimation is applied to tune the antecedent parameters. The Ninapro data is adopted to test the performance of the proposed model, which realizes recognition of 52 gestures and achieves 95.04% categorization accuracy. Moreover, grasping experiments are conducted on computer, communication, and consumer electronics (3C) experiment platform to test the ability of the classifier in real scenarios. The experiment recognizes six gestures. The results of the 3C grasping experiment show that the proposed method achieves 99.4% offline training accuracy as well as 96.07% online test accuracy. Meanwhile, 89.4% of the classification results can be obtained within 0.5 s. The overall results demonstrate great potential for real-world applications, such as human intent detection and manipulator control.

Future autonomous vehicles will incorporate high date rate communications and high-accuracy radar sensing capabilities operating in the millimeter-wave (mmWave) and higher frequencies, which results in Integrated sensing and communication (ISAC). Hybrid beamforming (HBF) is an attractive technology for practical vehicular ISAC systems. The HBF with the partially-connected structure (PCS) can effectively reduce the hardware cost and power consumption compared to fully-connected structure (FCS). But the constant-modulus constraint caused by PCS makes the HBF design problem non-convex, which poses a greater challenge. In this paper, we consider the HBF design with PCS as a weighted minimization problem of the communication and radar beamforming errors under the constant-modulus constraints and power constraints. Dual functions of communication and radar are expressed as a tradeoff in this question. Despite the optimization problem being non-convex and hard to obtain the global minimizer, we reduce the problem into a two-step subproblem including the analog precoder design and digital precoder design. Then, a variable projection-based alternating minimization algorithm is proposed to solve these problems. Unlike previous works, which focused on the relationship between variables to iteratively solve, our method exploits the intrinsic geometric features of the mmWave channel and the variable projection to simplify the solution of the beamformers. Simulation results demonstrate that the proposed algorithm achieves significantly improved performance in terms of the system spectral efficiency over the existing solutions and greatly reduces the computational complexity.

Platoon control is widely studied for coordinating connected and automated vehicles (CAVs) on highways due to its potential for improving traffic throughput and road safety. Inspired by platoon control, the cooperation of multiple CAVs in conflicting scenarios can be greatly simplified by virtual platooning. Vehicle-to-vehicle communication is an essential ingredient in virtual platoon systems. Massive data transmission with limited communication resources incurs inevitable imperfections such as transmission delay and dropped packets. As a result, unnecessary transmission needs to be avoided to establish a reliable wireless network. To this end, an event-triggered robust control method is developed to reduce the use of communication resources while ensuring the stability of the virtual platoon system with time-varying uncertainty. The uniform boundedness, uniform ultimate boundedness, and string stability of the closed-loop system are analytically proved. As for the triggering condition, the uncertainty of the boundary information is considered, so that the threshold can be estimated more reasonably. Simulation and experimental results verify that the proposed method can greatly reduce data transmission while creating multi-vehicle cooperation. The threshold affects the tracking ability and communication burden, and hence an optimization framework for choosing the threshold is worth exploring in future research.

This letter proposes a multipath subspace projection andextreme learning machine(ELM)-based indoor fingerprint localization algorithm called MuSpel-Fi, where thechannel state information(CSI) is utilized as the raw data to establish fingerprints. In this algorithm, the CSI is firstly organized into a time-domain matrix and then is projected into a subspace. This processing not only preserves the channel multipath information as much as possible, but also reduces the data dimension. Based on the reduced dimension projected data, the ELM network is exploited to implement the fingerprint localization. Considering the limited performance of a single ELM network, multiple ELM networks are jointly optimized to improve the localization performance. The experimental results demonstrate that the proposed MuSpel-Fi algorithm has higher positioning accuracy than traditional ones.

Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and DDPG simultaneously at each frame. Given a current state, two actions are obtained from CACC and DDPG, respectively. Then, an optimal action, corresponding to the one offering a larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a rule is designed to ensure that the change rate of acceleration is smaller than the desired value. Therefore, the proposed strategy not only guarantees the basic performance of car-following through CACC but also makes full use of the advantages of exploration on complex environments via DDPG. Finally, simulation results show that the car-following performance of the proposed strategy is improved compared with that of DDPG and CACC. Note to Practitioners—This article presents a new car-following strategy, which avoids the impact of deep deterministic policy gradient (DDPG) performance degradation on the system. In the proposed strategy, DDPG is replaced with cooperative adaptive cruise control (CACC) when the performance of DDPG is worse than that of CACC. Meanwhile, a switching rule is designed to guarantee that the change rate of acceleration is smaller than the threshold. Simulation results show that the performance of hybrid car-following strategy has been improved compared with that of only using CACC or DDPG. Moreover, the proposed strategy has the advantages of low computational burden, high real-time performance, and good scalability.

Uncertainty is a major concern in vehicle path tracking control design. The coefficients of the uncertainty bound are unknown. They are assumed to lie within prescribed fuzzy sets. First, based on the path tracking kinematic model, this article innovatively formulates the vehicle path tracking task as a constraint-following problem. Second, we put forward a deterministic adaptive robust control law with a tunable parameter to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop system. Third, an optimal scheme for the tunable parameter is proposed based on the fuzzy uncertainty. The resulting optimal robust control (ORC) minimizes a comprehensive fuzzy performance index that involves the fuzzy system performance and the control cost. The results of the CarSim-Simulink cosimulation and the hardware-in-loop experiment together show that the proposed ORC exhibits a superior path tracking performance.

To improve the reliability of the shared steering control system, and weaken the influence of uncertain driver behavior on driving safety, an extended shared steering control system with an authority adaptive allocation model is proposed. The system framework consists of upper-level and lower-level. The upper-level is an adaptive authority allocation model, which is composed of a reference model and an authority dynamic allocation model. The reference authority level can be calculated by the reference model, which is used as the reference of the authority dynamic allocation model. The authority levels of the autonomous controller and driver are gained by the adaptive authority allocation model. The lower level is the human-machine shared steering control, which includes the driver and autonomous controller. Using the authority level obtained by the upper-level, the lower-level can realize the coordinated control of human and machine. The effectiveness of the proposed approach is verified by simulation. The results show that relative to the numerical function model, the proposed approach can effectively weaken the impact of driver misoperation and poor preview time, the trajectory tracking ability and handling stability are improved significantly, and the human-machine conflict and the workload of the controller are weakened.

The optimal velocity-based model has been successfully applied to distributed car-following systems. However, the car-following performance is inevitably affected by a series of disturbances, particularly, sudden velocity changes of leading vehicle. To improve accuracy and response rate of car-following control in the presence of such disturbances, an improved super-twisting compensator (ISTC) is proposed and a composite controller is designed by combining ISTC with a finite- time controller. A second-order nominal system is constructed by using a virtual measurement signal along with its integration to facilitate the design of ISTC. By introducing the feedback of high-order estimation error, the accuracy and response rate of ISTC are increased significantly as compared with the conventional one under same gains. Such improvement further enhances the disturbance rejection ability of the composite controller. Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.

This paper presents a robust path tracking control method by utilizing the ideology of constraint-following approach for uncertain underactuated autonomous vehicles. The uncertainties are bounded with an unknown boundary. They do not all fall within the range space of the input matrix. Based on kinematic relations between the desired path and vehicle, the path tracking task is transformed into an equality constraint of the vehicle lateral dynamics states. The control goal is to make the underactuated vehicle follow the constraint, thus realizing the desired tracking performance. The constraint-following robust control (CFRC) is designed in two steps. First, a servo control design for the nominal system is devised without considering uncertainty and initial constraint deviation. Second, the uncertainty is meticulously decomposed into matched and mismatched portions based on the geometric structural characteristics of the constraint dynamics system. As a result, since the mismatched uncertainty is orthogonal to the constraint-following geometric space, it “disappears” in the stability analysis. The matched uncertainty is estimated by a self-adjusting leakage type adaptive law. On this basis, a robust control is designed based on the estimated matched uncertainty. Through Lyapunov minimax analysis, the proposed control method guarantees the approximate constraint-following performance. Finally, the TruckSim–Simulink co-simulations and real vehicle experiments are presented. The results show that the proposed control can robustly realize excellent tracking performance in the presence of time-vary uncertainties.

The upper-limb prosthesis has been extensively studied using electromyography (EMG) signals to overcome the physical and functional deficiencies of amputees in recent years. However, most studies focus on the discrete classification of gestures and ignore the interconnection between the classification results and the neuroprosthesis control interface, which plays a vital role in system development. In this article, a new continuous control scheme is proposed to achieve an effective control of the developed upper-limb prosthesis. It utilizes eight channels of EMG signals of the human upper limb to model and control the developed prosthesis. A continuous control scheme is proposed that combines the state of the system and the decoding results to dynamically produce the expected angular velocity of the joint based on the results of the classification. Finally, experiments are performed to demonstrate the effectiveness of the proposed algorithm using an upper-limb neuroprosthesis, achieving the reach and grasp tasks. The results showed that it improves performance with a regular angular velocity of the joint, which underlines the importance of an adequate control scheme for the EMG-guided prosthesis.

Mechanical systems with (possibly fast) time-varying but bounded uncertainty are considered. The exact value of the boundary is unknown. All the designer can get is that the boundary values are in a (known) fuzzy set. On the basis, a robust control method is proposed, which can ensure the uniformly bounded and uniformly ultimately bounded of the controlled mechanical system. The control contains two flexibly selectable design parameters. We seek to choose the optimal parameters. For a superior performance, two fuzzy-set-based performance indices are proposed to reflect the transient performance as well as the steady-state performance. Each performance index can be influenced by two design parameters. However, influences are nonconciliatory. This poses a design dilemma: the increase of a parameter may harness one performance while inflict the other. Therefore, the “optimal” choice of the design parameters is not intuitively clear. To resolve this dilemma, the two-player Nash-based noncooperative game theory is adopted, which is a notable feature of this article. Once the problem is formulated, we show that there is always a Nash-equilibrium solution to the two-player problem. We also show how to find it. The approach is very general, which also can be extended to$n$-player Nash game for future research. The control is applied to a compressor powered by permanent magnet synchronous motor (PMSM) as a demonstration. The resulting performance shows that this Nash-based robust control design is both practical and effective.

This article proposes a new robust control design framework for uncertain mechanical systems, which may be fully actuated or underactuated. The uncertainty is (possibly fast) time varying, which lies in prescribed fuzzy sets (hence fuzzy mechanical systems) and may be unbounded. The control goal is formulated as servo constraints (hence constraint-following control), which may be holonomic or nonholonomic. We introduce the possibility theory into the Lyapunov stability analysis (LSA), proposing possibility-based LSA (PBLSA), which allows a maximum failure possibility (generally small) prescribed by designers. It can be viewed as a generalization of the conventional LSA, and the resultant performance is interpreted in the context of possibility. By the PBLSA, a class of robust constraint-following controls that isnotIF–THEN heuristic rules based is proposed, which renders approximate constraint following for the system performance with a prescribed maximal failure possibility. Optimal design of a control parameter considering both system performance and control cost is investigated. The benefits of the proposed design framework are discussed and simulations on two applications are given for demonstrations.

Railway is regarded as the most sustainable means of modern transportation. With the fast-growing of fleet size and the railway mileage, the energy consumption of trains is becoming a serious concern globally. The nature of railway offers a unique opportunity to optimize the energy efficiency of locomotives by taking advantage of the undulating terrains along a route. The derivation of an energy-optimal train driving solution, however, proves to be a significant challenge due to the high dimension, nonlinearity, complex constraints, and time-varying characteristic of the problem. An optimized solution can only be attained by considering both the complex environmental conditions of a given route and the inherent characteristics of a locomotive. To tackle the problem, this paper employs a high-order correlation learning method for online generation of the energy optimized train driving solutions. Based on the driving data of experienced human drivers, a hypergraph model is used to learn the optimal embedding from the specified features for the decision of a driving operation. First, we design a feature set capturing the driving status. Next all the training data are formulated as a hypergraph and an inductive learning process is conducted to obtain the embedding matrix. The hypergraph model can be used for real-time generation of driving operation. We also proposed a reinforcement updating scheme, which offers the capability of sustainable enhancement on the hypergraph model in industrial applications. The learned model can be used to determine an optimized driving operation in real-time tested on the Hardware-in-Loop platform. Validation experiments proved that the energy consumption of the proposed solution is around 10% lower than that of average human drivers.

In many classification tasks, the misclassification costs of different categories usually vary significantly. Under such circumstances, it is essential to identify the importance of different categories and thus assign different misclassification losses in many applications, such as medical diagnosis, saliency detection and software defect prediction. However, we note that it is infeasible to determine the accurate cost value without great domain knowledge. In most common cases, we may just have the information that which category is more important than the other categories, i.e., the identification of defect-prone softwares is more important than that of defect-free. To tackle these issues, in this paper, we propose a hypergraph learning method with cost interval optimization, which is able to handle cost interval when data is formulated using the high-order relationships. In this way, data correlations are modeled by a hypergraph structure, which has the merit to exploit the underlying relationships behind the data. With a cost-sensitive hypergraph structure, in order to improve the performance of the classifier without precise cost value, we further introduce cost interval optimization to hypergraph learning. In this process, the optimization on cost interval achieves better performance instead of choosing uncertain fixed cost in the learning process. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.

As the most sustainable means of modern transportation, the railway trains are eagerly approaching autonomous driving due to their congenital advantages on operating environments compare to, e.g., road traffics. The intelligent automatic train driving aims at train control with a goal of energy efficiency, punctuality and safety. The derivation of an optimized train driving solution by taking advantage of the undulating terrains along a route, however, proves to be a significant challenge due to the high dimension, nonlinearity, complex constraints, and time-varying characteristic of the problem. To tackle the problem, we propose a two-level human driving experience learning framework and employ the fuzzy rule induction method for online generation of the optimized driving solutions. Based on the records of experienced human drivers, a FURIA model was built to learn the driving rules indicating the correlation between the specified features to the decision of a driving sequence. The fuzzy rules can generally find the best-match driving operation under certain running circumstances. The learned model can be used to determine an optimized driving operation in real-time. Validation experiments show that the energy consumption of the proposed solution is around 8.93% lower than that of average human drivers.

Intelligent transportation has become an essential field of cyber-physical systems. Among various intelligent transportation technologies, the automated highway system (AHS) has its unique advantage of being able to coordinate a platoon of vehicles as a whole unit. The major challenge of building a robust AHS is the nonlinear and (potentially) fast time-varying uncertainty induced by parameter variations and external disturbances. Finally, reflected as the spacing between neighboring vehicles, such uncertainties can be a serious concern for maintaining safety. This paper addresses the problem by proposing a mathematical transformation scheme to bound the spacing error and build a distributed control algorithm on such a basis. The propose algorithm achieves a spacing error satisfying both uniformly boundedness and uniformly ultimate boundedness. Our decentralized algorithm is communication efficient in the sense that it only requires the state information of the preceding car and the acceleration feedback and does not need to communicate with all other cars.

Railway transportation systems are the backbone of smart cities. With the rapid increasing of railway mileage, the energy consumption of train becomes a major concern. The uniqueness of train operations is that the geographic characteristics of each route is known a priori. On the other hand, the parameters (e.g., loads) of a train varies from trip to trip. Such a specialty determines that an energy-optimal driving profile for each train operation has to be pursued by considering both the geographic information and the inherent train conditions. The solution of the optimization problem, however, is hard due to its high dimension, nonlinearity, complex constraints and time-varying characteristics of a control sequence. As a result, an energy-saving solution to the train control optimization problem has to address the dilemma of optimization quality and computing time. This work proposes an energy-efficient train control framework by integrating both offline and onboard optimization techniques. The offline processing builds a decision tree based sketchy solution through a complete flow of sequence mining, optimization and machine learning. The onboard system feeds the train parameters into the decision tree to derive an optimized control sequence. A key innovation of this work is the identification of optimal patterns of control sequence by data mining the driving behaviors of the experienced train drivers and then apply the patterns to online trip planning. The proposed framework efficiently find an optimized driving solution by leveraging the training results derived with a compute-intensive offline learning flow. The framework was already testified in a smart freight train system. It was demonstrated an average of$9.84$percent energy-saving can be achieved.

A novel approach for optimal robust control of a class of generalized fuzzy dynamical systems is proposed. This is a novel use of fuzzy uncertainty in doing dynamical system control. The system may have nonlinear nominal terms and the other terms with uncertainty, including unknown parameters and input disturbances. The Fuzzy sets theory is creatively employed in presenting the system parameter and input uncertainty, and then the control structure is deterministic (versus if-then rule-based as is typical in Mamdani-type fuzzy control). The desired controlled system performance is also deterministic, with guaranteed performances of uniform boundedness and uniform ultimate boundedness. Fuzzy informations on the uncertainties are used in searching optimal control gain under a proposed LQG-like quadratic cost index. The control gain design problem is formulated as a constrained optimization problem with the solution be proved to be always existed and unique. Systematic procedure is summarized for such control design.

We propose a new approach for the control design of fuzzy dynamical systems. The system may contain uncertainty, which includes unknown parameter and input disturbance. The uncertainty lies within a prescribed fuzzy set. The control structure is deterministic, and, hence, not if-then rule-based. The desired controlled system performance includes uniform boundedness and uniform ultimate boundedness. In addition, we propose a quadratic cost index, which reflects the fuzzy system performance. We then formulate a control parameter design problem as a constrained optimization problem. It is proven that the global solution to this problem always exists and is unique. The closed-form solution and the closed-form minimum cost are derived.