Road depth estimation plays a crucial role in vehicle chassis control and autonomous driving. Monocular depth estimation, due to its low cost, energy efficiency, and ease of deployment, remains an important area for research. However, challenges persist in improving the accuracy of monocular depth estimation, reducing the impact of errors, and ensuring the convergence of errors over long-term operation. This paper proposes an improved monocular depth estimation approach that integrates deep learning techniques with feedback from vehicle suspension states. Under the DispNet architecture, we replace traditional neural networks with the Standard Nonlinear Operator Form (SNOF) and incorporate vehicle suspension information to build a delayed state observer for real-time depth estimation error compensation. State compensation is achieved through solving Linear Matrix Inequalities (LMI), ensuring error convergence during extended operation. Extensive real-world experiments using signal-collecting vehicles demonstrate that the proposed method exhibits excellent generalization capabilities across diverse environments and lighting conditions.

Decentralized Identity (DID) enhances authentication and privacy by empowering individuals to control their own digital identities, which has gained traction globally. To our knowledge, this paper presents the first end-to-end verification effort (from design to implementation) of a real-world Decentralized Identity (DID) protocol following the IIFAA DID standard, which has been deployed within the widely used super app Alipay and issued millions of DIDs in practice. We integrate formal verification into the development lifecycle of such industrial security protocol to systematically enhance its reliability from two levels: (1) At the design level, we utilized state-of-the-art protocol design verifier Tamarin to formally model the IIFAA DID standard under a realistic threat model tailored for super apps. We then formulated and performed automated verification of desired security properties using Tamarin. We identified several design flaws that could lead to a security breach. These issues were reported to the design team and have been addressed in the updated design. (2) At the implementation level, we first extract the desired specification derived from the verified symbolic model of protocol design in the form of a set of intermediate I/O specifications. Subsequently, we translate the I/O specifications into a set of functional specifications at the implementation level, which can then be verified by the automated tool VeriFast. We identified several inconsistencies between the implementation and the verified design which are fixed by the development team and led to verified implementation faithfully obeying the verified design, together offering an end-to-end verified secure DID protocol in Alipay super app. Our work showcases how an industrial security protocol development team can design and implement a practical verified secure Decentralized Identity (DID) protocol with the help of end-to-end formal verification.

As an important part of the motion system in high-end manufacturing equipment, dual-drive gantry stage urgently requires higher performance. Planar rapid positioning is a classical application of the gantry stage, while unmodeled dynamics, coupled dynamics of mechanism, and conflict between rapidity and precision bring many difficulties into controller design. In this article, a coupled dynamic model is established and transformed for positioning to provide better guidance for controller design under the rotation mode of the cross beam. A composite positioning control method consisting of a high-speed moving section and a high-precision positioning section is proposed for linearmotors on the gantry to achieve both fast response and precise control simultaneously. In addition, a synchronization scheme is proposed for the dual-driven cross beam with coupled dynamics to enhance the positioning precision of the workbench and balance of the beam. Experiments are conducted on the$X$-axis with one linearmotor for the composite positioning method and$Y$-axis with two linearmotors for the synchronization scheme combined with the composite positioning control separately, through which the effectiveness and validity of the proposed method are verified.Note to Practitioners—Planar positioning of dual-drive gantry stage is a significant technical problem in industrial applications, which pursues the ultimate speed and precision performance. In this paper, a dynamic model with rotational mode is established and transformed for positioning in an intuitive way. In addition, a composite positioning method is proposed for linearmotors on the stage to achieve high-speed and high-precision performance in the case of common nonlinear dynamics and disturbances. And then, to achieve high positioning performance of the cross beam while maintaining the balance, a synchronous scheme incorporated with the composite positioning method is presented, based on the transformed model. In industrial scenarios, the proposed method is very practical since a precise, rapid and stable point-to-point planar motion can be obtained only with conventional dynamic models and parameters, and it is crucial for production efficiency and quality.

Complex uncertain dynamics and external disturbances have always been critical factors restricting the performance improvement of dual-drive gantry system (DDGS), especially the synchronization and robustness. To enhance the performance of DDGS in uncertain industrial environments, in this paper, a high-performance robust control method is proposed based on a novel composite adaptive B-spline wavelet neural network (CABWNN) estimator. Specifically, a composite adaptive robust synchronization control method with desired compensation is proposed based on a rotational dynamic coupling model of the system. This method combines the benefits of both direct and indirect adaptive robust control, achieving superior control performance while simultaneously mitigating the impact of measurement noise in the system. Additionally, a novel CABWNN estimator with a specially designed composite adaptive weight updating law is introduced to approximate the system uncertainties, which enhances the approximation accuracy. Under the proposed control method, the boundedness of all signals in the closed-loop has been proved in theory. Furthermore, comparative experiments on a dual-drive gantry system validate the superiority of the proposed control strategy.

In this paper, a joint fault-tolerant and fault diagnosis strategy is proposed for handling multiple actuator faults in full-vehicle active suspension systems. Different from traditional methods where fault detection and isolation must precede the fault-tolerant control to provide the latter with certain fault information, our proposed scheme performs both jointly over the whole operation process with guaranteed suspension performance. For the fault-tolerant control, we develop high gain filters under a fast timescale to estimate the variations of the integrated control inputs caused by actuator faults, and the equivalent fault-free parts of the integrated control inputs are selected as the target control variables. In such a decomposition, we are able to determine the control law of each actuator without using the information of the faulty actuators. Under the framework of the fault-tolerant algorithm, we construct a bank of adaptive fault diagnosis observers to online identify the force constants of the actuators, where information of the heave, pitch, and roll motions are all utilized to ensure persistent excitation and enhance identification accuracy. In particular, actuator faults can be detected via the estimation of the force constants, in which both the location and severity of the actuator faults can be further identified. Subsequently, control commands are reassigned to alleviate the use of faulty actuators and thus protect them from further damage. The effectiveness of our proposed method is validated via extensive simulation results.Note to Practitioners—Satisfactory performance of vehicle active suspension systems is founded upon good reliability and safety of the suspension systems, which motivates us to conduct deep research into fault diagnosis and fault-tolerant problems to improve the significant system reliability. In this paper, a joint fault-tolerant and fault diagnosis (joint FTFD) scheme is proposed for addressing multiple actuator faults of full-vehicle active suspension systems. The proposed method can be used to guarantee suspension performance over the whole operation period even if multiple actuator faults occur, and the location and severity of the actuator faults can be identified at the same time. Different from the traditional methods where fault diagnosis precedes the fault-tolerant control to provide certain fault information, in the proposed joint FTFD scheme, fault diagnosis is carried out under the always-on fault-tolerant framework to ensure system reliability and performance over the whole operation process. The multi-timescale technique is used to compensate for actuator failure and achieve fault-tolerant control. Under the fault-tolerant framework, actuator faults can be detected and both the location and severity of the faults are decided via monitoring the online identification results of the motor force constants. However, one limitation of the proposed method is the influence of sensor noise on high gain filters in real world application, which might degrade fault-tolerant performance. For the future research, one significant work is to carry out real vehicle tests to promote the proposed algorithm to practical application. Also, fault diagnosis and fault-tolerant control for suspension sensor faults is another valuable topic which deserves our efforts. Given that the proposed fault-tolerant framework is independent on a particular control system or a specific type of nominal controller, the fault-tolerant design idea can be transplanted to a broad range of systems besides addressing suspension actuator faults.

This article studies finite-time adaptive fault-tolerant control for uncertain robotic manipulator systems with guaranteed transient performance. Combining with backstepping method and neural network techniques, a novel finite-time adaptive fault-tolerant control method is presented, where neural networks are utilized to handle model uncertainties. By introducing an error transformation strategy and a performance function, the transient performance constraints of the system are converted into the stabilization problem of the unconstrained robot manipulator. In addition, adaptive fault-tolerant control weakens the effect of actuator failures on control performance, and a novel adaptive upper bound estimation strategy is adopted to compensate for neural network training errors and external disturbances. Subsequently, finite-time control ensures that the position tracking errors can converge to a small neighborhood around zero within a finite time and guarantees the required tracking performance. Finally, a simulation is conducted based on an actual two-link manipulator model to prove the superiority of our control approach, and the validity of the control approach is further verified on the Franka Emika Panda robot.

Incorporating both RGB and depth images has proven effective for enhancing the performance of semantic segmentation. However, current RGB-D semantic segmentation methods tend to overlook the critical role of cross-modal difference information during fusion, leading to the undesired suppression of discriminative cues and a failure to achieve potent cross-modal complementary fusion. In this article, a novel RGB-D semantic segmentation approach that realizes the efficient utilization of multimodal information is proposed. To address the issue of the suppression of cross-modal difference information, we propose a dynamic frequency-spatial difference-aware fusion module adept at explicitly emphasizing cross-modal differences, capturing vital features in the frequency domain, and using them to aggregate spatial context information of multimodal features. We also present a novel soft-edge loss to meticulously handle complex scenes by supervising different regions respectively. In addition, a progressive calibration context module is designed to enhance global contextual information by capturing multiscale multimodal representations. Extensive experiments on two public RGB-D datasets demonstrate that the proposed DFNet achieves highly competitive performance compared to state-of-the-art methods, making it well-suited for assisting indoor robots.

Monocular 3D object detection finds applications in various fields, notably in intelligent driving, due to its cost-effectiveness and ease of deployment. However, its accuracy significantly lags behind LiDAR-based methods, primarily because the monocular depth estimation problem is inherently challenging. While some methods leverage additional information to aid in network training and enhance performance, they are hindered by their reliance on specific datasets. We contend that many components of monocular 3D object detection lack the necessary adaptability, impeding the performance of the detector. In this paper, we propose six adaptive methods addressing issues related to network structure, loss function, and optimizer. These methods specifically target the rigid components within the detector that hinder adaptability. Simultaneously, we provide theoretical insights into the network output and propose two novel regression methods. These methods facilitate more straightforward learning for the network. Importantly, our approach does not depend on supplementary information, allowing for end-to-end training. In comparison with existing methods, our proposed approach demonstrates competitive speed and accuracy. On the KITTI dataset, our method achieves a 17.72% AP3D(IOU =0.7, Car, Moderate), outperforming all previous monocular methods. Additionally, our approach prioritizes speed, achieving a runtime of up to 52 FPS on an RTX 2080Ti GPU, surpassing all previous monocular methods. The source codes are at:https://github.com/jiayisong/AMNet.

In autonomous driving systems, multispectral semantic segmentation integrates thermal images with RGB images to mitigate reliability degradation under challenging illumination conditions. However, most RGB-T methods cannot maintain high accuracy without sacrificing efficiency, thereby hindering efficient scene understanding. This paper presents RADNet, an asymmetric dual-stream network optimized for real-time RGB-T semantic segmentation, which incorporates three important components. The FFD module analyzes thermal features from the perspective of frequency to enhance thermal representations while suppressing interference. Furthermore, the ISD strategy explicitly transfers multimodal knowledge under varying illumination, leveraging the strengths of each modality without adding inference overhead. In addition, the FR module combines standard convolutions and central difference convolutions via structural re-parameterization to enhance detailed information. RADNet shows noticeable efficiency with 198.49 FPS on an RTX 3090 GPU while maintaining 13.21M parameters. Experimental results across three multispectral benchmarks covering diverse scenarios and illumination variations validate its competitive accuracy. Our network strikes a promising balance between segmentation performance and computational efficiency, making it a promising solution for further exploration in autonomous driving systems.

Deep feature fusion plays a significant role in the strong learning ability of convolutional neural networks (CNNs) for computer vision tasks. Recently, works continually demonstrate the advantages of efficient aggregation strategy and some of them refer to multiscale representations. In this article, we describe a novel network architecture for high-level computer vision tasks where densely connected feature fusion provides multiscale representations for the residual network. We term our method the ResDNet which is a simple and efficient backbone made up of sequential ResDNet modules containing the variants of dense blocks named sliding dense blocks (SDBs). Compared with DenseNet, ResDNet enhances the feature fusion and reduces the redundancy by shallower densely connected architectures. Experimental results on three classification benchmarks including CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of ResDNet. ResDNet always outperforms DenseNet using much less computation on CIFAR-100. On ImageNet, ResDNet-B-129 achieves 1.94% and 0.89% top-1 accuracy improvement over ResNet-50 and DenseNet-201 with similar complexity. Besides, ResDNet with more than 1000 layers achieves remarkable accuracy on CIFAR compared with other state-of-the-art results. Based on MMdetection implementation of RetinaNet, ResDNet-B-129 improves mAP from 36.3 to 39.5 compared with ResNet-50 on COCO dataset.

Hierarchical control is widely employed for redundant robots to manage multiple simultaneous tasks with distinct priority levels. A novel hierarchical optimal control strategy was recently introduced to achieve performance-optimal tracking under static and strict priority constraints. However, in complex and dynamic environments, robots must possess the capability to switch hierarchical behaviors online to adapt to varying operational scenarios. Existing continuous priority-switching methods often sacrifice hierarchical control performance and fail to asymptotically track the hierarchical optimal trajectory. In this article, a continuously shaping prioritized Jacobian algorithm is proposed and integrated into a newly developed continuous hierarchical optimal control framework with priority transitions. This approach not only ensures optimal control performance but also facilitates continuous priority switching. The continuity and accuracy of the proposed algorithm, as well as the bounded stability of the closed-loop system state variables, are thoroughly analyzed in this work. The effectiveness of the proposed method is validated through simulations and experiments on the Franka Emika Panda robot.

Autonomous vehicles (AVs) equipped with an array of advanced sensors gather road preview information, presenting new opportunities to enhance ride comfort. To simultaneously improve both the vertical and longitudinal ride comfort of vehicles, a dual timescale model predictive control (MPC) preview active suspension system (ASS) and longitudinal dynamics coupled controller is developed. On a short time scale, the coupling control of the vehicle’s ASS and longitudinal acceleration is achieved using road preview information, enhancing both vertical and longitudinal ride comfort, thereby improving response speed. On a longer time scale, road prediction information obtained via Gaussian processes (GPs) is utilized for vehicle speed planning, aiming to mitigate vertical excitations caused by road profile variations while minimizing frequent speed changes. However, when road preview information is continuously used as disturbance predictions in MPC, it undermines the recursive feasibility and stability of MPC. To address this, a scaling method is devised to account for disturbances incorporated into the predictive model. Theoretical foundations ensure both recursive feasibility and asymptotic stability. The effectiveness and advantages of the dual timescale MPC preview active suspension and longitudinal dynamics coupled control are validated through simulations and bench tests.

To improve the operational reliability of active sus-pension system, this paper develops a robust fault detection (FD) method for half-car active suspension system, which is robust to external disturbances and measurement noises. The proposed fault detection approach is developed with the help of a filtering technique. Firstly, the fault detection observer is applied to obtain estimation errors used for actuator fault detection. Then, the residual and detection threshold signals are obtained by filtering estimation errors within a specified frequency range. Specifically, when designing detection thresholds, the filtering technique is introduced to suppress the influence of external disturbances and measurement noises, which helps to reduce the conservatism of detection threshold, resulting in enhancing the ability of fault detection. In addition, the condition of fault detectability, which can quantitatively describe the class of detectable faults, is strictly established. Simulation example is conducted to verify the effectiveness of the proposed robust fault detection method.

This paper investigates the issue of event-triggered adaptive saturated fault-tolerant control (ESFC) for uncertain nonlinear systems with time-varying full state constraints (TFSCs), actuator saturation and faults as well as unknown control direction. A bounded function with an auxiliary variable is constructed by utilizing a novel dynamics of the auxiliary system, which contributes to reducing the adverse impact of actuator saturation. Different from the previous backstepping-based event-triggered control methods such specifications by either using fuzzy approximation or by employing neural approximation techniques, this paper skillfully addresses the unknown nonlinearities, actuator saturation and faults without involving any approximation structures, and thus, we proposes the ESFC on the basis of low-complexity design framework as contributing to communication and computational resource reduction. A rigorous theoretical analysis shows that the proposed control method is an effective way to handle with the problems of actuator saturation and faults, full state constraints, and unknown system uncertainties, while simultaneously simplifying the backstepping design and avoiding the issue of explosion of complexity. The asymptotic stability of the closed-loop system is guaranteed and the Zeno behavior can be effectively removed. We present an application example of a linear motor scenario to illustrate the effectiveness of the method.Note to Practitioners—Since state constraints, actuator saturation and faults, unknown mechanism model, and limited bandwidths exist extensively in practical engineering systems, which constantly degrade the operation performance of the plant. To handle these disadvantages, this paper is focus on providing simple but effective ESFC methods to ensure the asymptotic stability and enhance reliability. Compared to existing results, the presented method only uses the state signals of system without using system dynamic functions under mild conditions, which provides a theoretical basis, and has the advantages of low-complexity design, and easy implementation in practical engineering. Preliminary physical experimental comparisons demonstrate that this method is applicable to practical liner-motor platform, and achieves satisfactory control performance.

This paper develops an event-triggered fault-tolerant tracking strategy for block-triangular MIMO uncertain nonlinear systems with sensors and actuators polluting by multiplicative/additive faults and unknown control directions, to address the time-varying asymmetric output constraint control. By combining an adaptive fault-tolerant control law with event-triggered mechanisms (ETMs), the presented method possesses the properties of low structure and calculation complexity, and can effectively conserve the system resources of communication and computation. To solve the issues of unknown control directions and time-varying asymmetric output constraints, the proposed method utilizes the integrated design of the Nussbaum function and barrier Lyapunov function (BLF) to realize a novel constrained tracking control with strong robustness, and can eliminate the adverse effects of the output tracking caused by all state (except for output) sensor faults. The proposed controller operates without having to use any approximating techniques, has the capability to handle the coupling uncertain terms derived from unknown system functions, sensor and actuator faults, and ETMs, and avoids the issue of explosion of complexity as in traditional backstepping procedure. The closed-loop stability can be guaranteed based on Lyapunov stability analysis with contradiction, while ensuring the boundedness of all signals, maintaining the output constraint, and preventing the Zeno behavior. Finally, the potential of application is investigated by means of experiments on a Linear Motor system, illustrating the effectiveness. Note to Practitioners— Due to the existence of output and bandwidth constraints, sensor and actuator faults, and unknown system models in practical plants, the operational performance of the system may inevitably degraded. To address this issue, this study primarily focuses on developing a low-complexity adaptive control method that aim to guarantee the overall performance of the system. Existing approaches mainly rely on system dynamic functions or adopt the approximation technology, our methodology can only utilizes the state signals of the system, which possesses advantages of low-complexity design and easy implementation. Preliminary experiments conducted through physical experiments demonstrate the applicability of this method to practical linear-motor platforms, yielding satisfactory control performance.

Dual-drive crossbeam system has been an significant part of high-end equipment, whose synchronous accuracy and robustness determine the performance if equipment. Thus, a robust iterative learning control method is proposed, which is based on the cross-coupling model. Robust term is to deal with the non-repetitive disturbance and iterative learning term is to compensate the unknown dynamics by repeating the same task. Finally, different control methods are conducted to verify the effectiveness of the proposed method.

This paper proposed a neural networks-based adaptive control scheme for linear motors considering cogging force compensation. The cogging force is modeled and compensated for improving the tracking performance. An indirect parameter adaptive strategy is proposed to address the problem of parametric uncertainties. Compared with the direct adaptive strategy, this strategy can promote the parameter estimations to converge to the true values. In addition, radial basis neural networks are designed to estimate remaining system uncertainties, including unmodeled dynamics, model errors, and external disturbances. Comparative experiments are conducted on an iron-core permanent magnet linear synchronous motor platform. The experimental results show that the proposed control scheme can achieve excellent control performance.

Synchronization control and coordination control are the core issues of dual-linear-motor-driven gantries (DLMDG). This paper proposes a novel integrated realtime compensation super-twisting sliding-mode control (RCSTSMC) framework that aims to simultaneously improve the contouring accuracy and synchronization performance of DLMDG for highly repetitive tracking tasks. The established control scheme combines the fast convergence and immunity to disturbance of the higher order sliding mode and the advantage of model-based realtime compensation. The results of the simulation confirm the superiority of the RCSTSMC. Compared to the ARC, the RCSTSMC has improved synchronization performance and contouring performance by 72.43 % and 69.11 % respectively, and the maximum synchronization and contouring errors have been reduced by 5.19 μrad and 0.96 μm.

As a typical mechatronics system, dual drive gantry stage has been widely used in high-end intelligent equipment. In this paper, an adaptive robust synchronization control scheme based on RBF neural network is presented to improve the synchronization accuracy and robust performance of dual drive gantry system. In order to overcome the limitation of system performance caused by ignoring high-frequency rotation mode in traditional modeling, a more reasonable rotational dynamic coupling model of gantry table was established. In addition, the adaptive robust control method with expected compensation is adopted to avoid the interference of measurement noise in the system and realize accurate compensation of the model. The advantages of RBF neural network infinite approximation are used to deal with the effects of model compensation residual, unmodeled dynamics and uncertain disturbances. The stability of the closed-loop system is proved by the Lyapunov theorem. Finally, different control strategies are used to conduct comparative experiments and the experimental results verify the superiority and effectiveness of the proposed control strategy. Note to Practitioners—The synchronization problem of dual drive gantry stage is a research hotspot in the industrial field and its control accuracy and robustness are important indexes that affect the system performance. In this paper, the coupled dynamics model of the gantry system is analyzed and established. In addition, an adaptive robust synchronous control strategy based on RBF neural network is presented to deal with various nonlinearities, mechanical strong coupling constraints and external unknown disturbances in the system, which improves the synchronization accuracy and anti-interference ability of the system. In practical industrial application, the designed controller can be applied to a class of dual-drive gantry systems to ensure the quality of product processing. At the same time, under the strong disturbance of complex working conditions, machine damage or more serious safety accidents caused by asynchronous movement can be avoided and the system reliability can be effectively improved, which is of great significance to industrial production.

Camera and LiDAR are indispensable perception units in autonomous driving, providing complementary environmental information for 3D semantic segmentation. It is the key point that fuses the information of two modalities to accurate and robust semantic segmentation. However, three major factors will restrict the performance of fusion-based methods, i.e., the reliability of image features, the contribution of different image features, and the trade-off between results of image and point cloud. This paper proposes a novel multi-phase fusion network for 3D semantic segmentation. For the first factor, this paper takes the lead in regarding the problem that image features may be wrong due to the lack of dense annotations in the common datasets as a weak supervision problem and introduces the weakly supervised loss. Second, the proposed attention based feature fusion module can filter and reweight the image features effectively. Third, the results of the two modalities are further fused by self-confidence based late fusion module at pixel-level to complement their advantages. The proposed scheme has been evaluated on nuScenes and SemanticKITTI benchmarks, and the results show the competitiveness with state-of-the-art methods. The ablation studies demonstrate the superiority of the method in sparse classes segmentation. In addition, the robustness is also evaluated, and the results of the proposed method can keep relatively accurate even when faults in one of the sensors.

Autonomous driving has witnessed rapid development with the application of artificial intelligence technology in recent years. Lane detection is one of the tasks of environment perception, which affects the planning and decision-making directly, and requires the algorithm to meet both high precision and high efficiency. Most of the existing methods extract pixels belonging to lanes in the image, which should be postprocessed, otherwise it cannot be applied to subsequent tasks like planning. This article proposes an end-to-end lane detection method that utilizes auxiliary supervision and knowledge distillation based teaching-test module to predict the parameters of polynomials of lanes directly. The teaching-test module guides the polynomial regression branch to learn the shape features from the segmentation branch to improve the fitting accuracy under complex road conditions. The proposed method is validated on TuSimple and CULane datasets, and is competitive with state-of-the-art methods in efficiency and accuracy.

Using light-weight architectures or reasoning on low-resolution images, recent methods realize very fast scene parsing, even running at more than 100 FPS on a single GPU. However, there is still a significant gap in performance between these real-time methods and the models based on dilation backbones. To this end, we proposed a family of deep dual-resolution networks (DDRNets) for real-time and accurate semantic segmentation, which consist of deep dual-resolution backbones and enhanced low-resolution contextual information extractors. The two deep branches and multiple bilateral fusions of backbones generate higher quality details compared to existing two-pathway methods. The enhanced contextual information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) enlarges effective receptive fields and fuses multi-scale context based on low-resolution feature maps with little time cost. Our method achieves a new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset. For the input of full resolution, on a single 2080Ti GPU without hardware acceleration, DDRNet-23-slim yields 77.4% mIoU at 102 FPS on Cityscapes test set and 74.7% mIoU at 230 FPS on CamVid test set. With widely used test augmentation, our method is superior to most state-of-the-art models and requires much less computation. Codes and trained models are available athttps://github.com/ydhongHIT/DDRNet.

This article investigates the completely unknown autonomous vehicle tracking issues with actuator faults through model-free adaptive dynamic programming (MFADP) approaches. Because partial parameters are measured difficultly or inaccurately, the model-based control theories are imperfect for the vehicles. Therefore, the proposed multiplayer optimal control method in this work, which is not necessary to know the prior system knowledge, achieves the purpose of unknown vehicle tracking control via a novel MFADP theory. Besides, the control strategies are robust, which contain adaptive regulators to eliminate the disturbance of the vehicle systems caused by actuator faults, modeling errors, and curvature interference. To reduce the computational burden of control, a single neural network (NN) architecture is constructed with minimal computational cost and fast response speed. In addition, the convergence analysis of the NN structure, the stability and robustness analysis of identification, and the control schemes in this work are supplied. Finally, two driving scenario simulations are shown to prove the effectiveness of the established controller.

In this article, a complete parametric proportional-integral observer (PPIO)-based approach is proposed to improve the performance of gradient estimation for the fast extremum seeking (ES) scheme acting on a Hammerstein plant. Unlike the prevailing gradient estimation approach of the fast ES which uses a Luenberger observer without an explicit way to obtain the observer gains, a systemic complete PPIO is established based on a complete parametric solution to a type of generalized Sylvester matrix equations. The proposed PPIO presents complete parameterization of all the gain matrices as well as the left eigenvectors in terms of some sets of design parameters that represent the degrees of design freedom. Then, the gradient estimator is constructed by multiplying the states of PPIO and the demodulation signal. Moreover, a synthetic objective function, which includes weighted performance indices of the transient error and the steady-state accuracy, is formulated. The performance of the gradient estimator is improved by minimizing the synthetic objective function through adjusting the degrees of freedom of the PPIO, and all explicit values of the parametric gain matrices are derived with the adjusted degrees of freedom. In turn, a faster and more accurate gradient estimation scheme can be obtained and significantly improve the convergence of the closed-loop system. Besides, the proposed PPIO-based estimator has excellent performance under the noise condition. Simulation examples and an application to the lean-burn combustion system are used to illustrate the effectiveness of the proposed gradient estimation scheme.

Dual-drive gantry stage has been widely applied to various industrial manufacturing fields with its unique structural advantages, and the synchronous control accuracy of the platform is crucial to the performance of the whole motion system. Therefore, an adaptive robust synchronous control scheme based on an improved master-slave structure is proposed, which is not only simple in structure but also easy to implement in engineering. The error dynamics model established in this article makes up for the lag of response of traditional master-slave control and improves the stability of closed-loop system. Online parameter adaptive algorithms deal with parameter uncertainties in the system, while robust control deals with unmodeled dynamics and external disturbances. In addition, nonlinear cogging force compensation is applied to the gantry biaxial system to further improve the control accuracy of tracking and synchronization. Finally, a dual-drive gantry stage system with good tracking and synchronization performance is obtained. The effectiveness and superiority of the proposed control strategy are verified by the comparison of several groups of experiments.

In this article, an adaptive event-triggered fault-tolerant asymptotic tracking control problem guaranteeing prescribed performance is addressed for a class of block-triangular multi-input and multioutput uncertain nonlinear systems with unknown nonlinearities, unknown control directions, and actuator faults. Through a systematic co-design of the adaptive control law and the event-triggered mechanism, including fixed and relative threshold strategies, a control scheme with low structure and calculation complexity is designed to conserve system communication and computation resources. In this design, the output asymptotic tracking is achieved. The Nussbaum gain technique is incorporated to overcome unknown control directions with a new adaptive law, and a type of barrier Lyapunov function is adopted to handle the prescribed performance control problem, which contributes to a novel control law with strong robustness. The robust controller can address the uncertainties and couplings derived from the system structure, actuator faults, and event-triggered rules, without using approximating structures or compensators. Besides, the explosion of complexity is avoided. It is proved that all signals of the closed-loop system remain bounded, and system tracking errors asymptotically approach 0 with the prescribed performance, while the Zeno behavior is prevented. Finally, the effectiveness of the proposed control scheme is evaluated via an application example of the half-car active suspension system.

In this article, an adaptive sensor fault accommodation scheme is proposed for uncertain vehicle active suspensions via output-feedback control where vehicle body displacement is the only measurable output signal corrupted by sensor bias. An adaptive observer with variable gains is constructed to obtain state estimates whose design procedure involves parameter adaption of the uncertain system parameters and sensor bias, and an output-feedback controller is designed to attenuate the vehicle body displacement based on the partial measurement information, estimates of the states, and unknown parameters. Compensation for measurement error is made both in the design process of the adaptive observer and output-feedback controller in order to weaken the influence brought about by sensor bias fault. In order to guarantee system stability, the variable observer gains are determined in real time using a switching strategy where their values can be modified in finite times by monitoring the state estimates generated by the observer itself. It is proved that the vehicle body displacement will converge to a small neighborhood around zero, and all the signals of the closed-loop system are ensured to be bounded through selecting suitable control parameters. Simulation is carried out to show the effectiveness of the proposed method and results indicate that better stabilization of the suspension vertical motion can be achieved through adaptive compensation for sensor bias.

The main function of active suspension systems is to suppress vibration resulted from the road roughness and improve passengers’ ride comfort, and some time-domain constraints such as mechanical limitation should be taken into consideration when designing active controllers. In this article, a constrained adaptive backstepping control scheme is proposed for the quarter-car suspension with parameter uncertainties, in which the primary control objective is to stabilize the vertical motion of the vehicle body and the suspension mechanical structure constraint can be satisfied in the meanwhile, and energy analysis has been made to demonstrate the potential of energy regeneration and reducing energy consumption for the designed active suspension system. In terms of dealing with the hard constraint, a specific nonlinear filter is employed in order to integrate the main control objective and time-domain constraint into a single controlled variable. In addition, a barrier Lyapunov function is selected to make the defined controlled variable converge to zero and stay in the allowable limit, which means that the vertical motion of the vehicle body can be stabilized and the suspension deflection restriction will not be transgressed. Experiments are carried out on the active suspension test plant to verify the effectiveness of the designed control scheme. Since the high energy consumption of active actuators is one of the drawbacks to be overcome, the energy flow of the dc motors is analyzed in the latter part of this article so as to provide a theoretical basis for energy harvesting design in further study. Finally, the energy consumption of the active suspension plant in experiments is figured out and its potential of energy recovery is demonstrated in detail.

Modern autonomous vehicles are required to perform various visual perception tasks for scene construction and motion decision. The multiobject tracking and instance segmentation (MOTS) are the main tasks since they directly influence the steering and braking of the car. Implementing both tasks using a multitask learning neural network presents significant challenges in performance and complexity. Current work on MOTS devotes to improve the precision of the network with a two-stage tracking by detection model, which is difficult to satisfy the real-time requirement of autonomous vehicles. In this article, a real-time multitask network named YolTrack based on one-stage instance segmentation model is proposed to perform the MOTS task, achieving an inference speed of 29.5 frames per second (fps) with slight accuracy and precision drop. The YolTrack uses ShuffleNet V2 with feature pyramid network (FPN) as a backbone, from which two decoders are extended to generate instance segments and embedding vectors. Segmentation masks are used to improve the tracking performance by performing logic AND operation with feature maps, proving that foreground segmentation plays an important role in object tracking. The different scales of multiple tasks are balanced by the optimized geometric mean loss during the training phase. Experimental results on the KITTI MOTS data set show that YolTrack outperforms other state-of-the-art MOTS architectures in real-time aspect and is appropriate for deployment in autonomous vehicles.

Multilateral telerobotic system has potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback, and cooperative manipulation under time delays, various uncertainties, and external disturbance. In this paper, a novel radial basis function neural network (RBFNN) based adaptive sliding mode control design is proposed for nonlinear multilateral telerobotic system with n-master-n-slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (nonpower signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral telerobotic system with 2-master-2-slave manipulators is implemented. The results show the good performance of our design.

In this paper, an adaptive fault-tolerant method is proposed to synthesize a compensation controller for the uncertain nonlinear pure-feedback systems possessing dead-zone actuators and stochastic failures. Each actuator's failure mode is described by a scalar Markovian type function, which is not only much more practical in control engineering but also challenging in control theory. By exploring the adaptive backstepping methodology, a compensation scheme for actuator failure is presented to guarantee that the solution of the closed-loop system is a unique and bounded in probability. Importantly, the proposed control method can achieve arbitrarily small tracking error in the presence of nonlinear actuators with random failures. The case study of active suspension system using the adaptive fault-tolerant compensation controller shows the effectiveness of the presented method.

In this paper, an output feedback finite-time control method is investigated for stabilizing the perturbed vehicle active suspension system to improve the suspension performance. Since physical suspension systems always exist in the phenomenon of uncertainty or external disturbance, a novel disturbance compensator with finite-time convergence performance is proposed for efficiently compensating the unknown external disturbance. Moreover, the presented compensator is advantageous over the existing ones since it is continuous and can completely remove the matched disturbance. From the viewpoint of practical implementation, continuous control law will not lead to chattering, which is desirable for electrical and mechanical systems. For the nominal suspension system without disturbance, a homogeneous controller with a simple filter is constructed to achieve a finite-time convergence property, where the filter is applied to obtain the unknown velocity signal. Thus, the nominal controller combines a disturbance compensator into an overall continuous control law, which provides two independent parts with a separate design unit and a high flexibility for selecting the control gains. According to the geometric homogeneity and finite-time separation principle, it can be shown that the active suspension is finite-time stabilized. A designed example is given to illustrate the effectiveness of the presented controller for improving the vehicle ride performance.

In this paper, sampled-data adaptive robust control is proposed for a class of uncertain cascaded nonlinear system with states and inputs constraints. The systematic design procedure can be divided into two steps: i) design a sampled-data adaptive robust controller for the plant to not only stabilize the closed-loop system but also track the desired command although there are a variety of uncertainties and disturbances in the system; ii) design a reference governor for the control system to avoid the states and inputs violating their limits. Finally, the proposed method is employed in Motor-servo system to demonstrate the effectiveness.

Suspension operation reliability is one of the most significant performance indexes that concerns maneuvering stability and drive safety. In this paper, in order to guarantee good suspension reliability, an adaptive fault detection and isolation scheme is proposed for quarter-car active suspension systems with parametric and nonlinear uncertainties. To realize fault diagnosis for active suspensions, an adaptive fault detection estimator and several fault isolation estimators are designed to generate state residuals. Corresponding adaptive thresholds are developed to help judge the occurrence and type of possible faults. In the process of constructing state estimators, the uncertain parameter is updated online so that good sensitivity of fault detection can be achieved. Illustrative simulation is carried out to validate the effectiveness of the fault diagnosis scheme, and results indicate that the proposed method is sensible to sudden faults and maintain robustness to model uncertainties appearing in suspension systems.

Three-dimensional (3D) morphological information of Zebrafish larvae is important for investigating the development of the vertebrate model. Some existing automated handling systems have already been developed to reconstruct 3D models of micro-objects, but many commercial devices are generally costly and complicated to assemble, which limits their wide usage. In this paper, we present an automated visual servo platform to carve 3D model of Zebrafish larva in a simple and controllable manner. The proposed 3D carving strategy only involves a 4-DOF manipulator, a glass capillary and a micropump. The Zebrafish larva is first captured by the capillary mounted at the end of the manipulator. Then, the manipulator rotates larva body to desired orientations in order to obtain 2D images from different views. A structure-from-motion algorithm finally carves the 3D model of the larva body. Experimental results verify the validity of proposed methods, and a guideline of selecting the number of views is also given. As a high-cost-performance system, it has a considerable reference for reconstructing other microobjects.

Sparse unmixing algorithm aims at finding the optimal subset of signatures from a spectral library to best model each pixel in hyperspectral image and estimating their corresponding abundance. However, the high mutual coherence of spectral library limits the performance of sparse unmixing algorithm. In this paper, a method referencing the extracted information from hyperspectral image was managed to prune spectral library (REiPSL) for improving the accuracy and efficiency of sparse unmixing. The REiPSL method was applied to the simulated hyperspectral data and Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image. The experiments show that the accuracy and efficiency are improved by comparing with the results obtained using the complete spectral library directly.

Nocturnal lighting is directly related to human activity and has been used in modeling population density. Mismatch of spatial scale between county and pixel results in noticeable discrepancy in Digital Number (DN). Consequently, this paper focuses on the limitation and proposes a method to model population density using the DMSP-OLS night-time data. MODIS land cover product was employed to identify habitable area. Population density in habitable areas was simulated using mean DNs of night-time image and mean populations at county level so as to eliminate the discrepancy in DN. The Pearl River Delta of China is taken as study area. The accuracy of estimation was assessed with census data. The results indicated the proposed method has potential in estimating population density and differences among cities need to be considered in modeling.

This paper is concerned with the problem of adaptive tracking control for a class of nonlinear systems with parametric uncertainty, bounded external disturbance, and actuator saturation. In order to achieve robust output tracking for the saturated uncertain nonlinear systems, a combination of adaptive robust control (ARC) and a novel terminal sliding-mode-based nonlinear disturbance observer (TSDO) is proposed, where the modeling inaccuracy and disturbance are integrated as a lumped disturbance. Specifically, the observer errors of estimating the lump disturbances converge to zero in finite-time for improving the precision of estimation. The estimated disturbances are then used in the controller to compensate for the system's lumped disturbances. The analytical results show that the proposed scheme is stable and can guarantee the asymptotic tracking with the tracking error converging to zero even in the presence of disturbances. Finally, the developed method is illustrated the effectiveness by the application to control of a quarter-car model with active suspension system.

This paper presents the problem of finite-time stabilization for vehicle suspension systems with hard constraints based on terminal sliding-mode (TSM) control. As we know, one of the strong points of TSM control is its finite-time convergence to a given equilibrium of the system under consideration, which may be useful in specific applications. However, two main problems hindering the application of the TSM control are the singularity and chattering in TSM control systems. This paper proposes a novel second-order sliding-mode algorithm to soften the switching control law. The effect of the equivalent low-pass filter can be properly controlled in the algorithm based on requirements. Meantime, since the derivatives of term with fractional power do not appear in the control law, the control singularity is avoided. Thus, a chattering-free TSM control scheme for suspension systems is proposed, which allows both the chattering and singularity problems to be resolved. Finally, the effectiveness of the proposed approach is illustrated by both theoretical analysis and comparative experiment results.