Considering situations such as sensor failures and communication losses, limited data samples are a common challenge in actual industrial processes, making the traditional integrated architecture of fault detection (FD) and fault-tolerant control (FTC) based on subspace identification difficult to be applicable. Regarding this problem, this article proposes a nuclear norm-based subspace identification method for FD and FTC. This method leverages key structural matrix properties in the input and output data model, alleviating reliance on data samples. The parameter matrices required to construct the fault detector and fault-tolerant controller can be directly identified within the nuclear norm optimization framework, enabling the design of an integrated FD and FTC architecture. Two case studies demonstrate that the developed method enhances detection and control performance compared with traditional subspace identification methods, particularly in the case of limited data samples.

This paper proposes a trajectory-attracted adaptive tracking control (TAATC) scheme based on a hybrid guiding vector field (HGVF) for robotic systems, addressing the high operational difficulty and safety concerns inherent in flexible environments. The HGVF is constructed using the characteristics of different task spaces in flexible environments to enable smooth transitions between free space and contact space with adjustable operating velocity, thereby enhancing robustness against environmental interactions and uncertainties. The HGVF can attract the state trajectories of the robotic systems by path convergence of an auxiliary dynamic system, simplifying the trajectory planning process with a time-independent representation of the desired path. In addition, a neural network is employed to compensate for uncertainties in the robotic systems, while a nonlinear duffing function represents the dynamic contact force model of flexible environments. In this way, the TAATC scheme is constructed by using the HGVF and the adaptive neural network, which unifies trajectory planning and tracking control. By using the proposed TAATC scheme, the robotic systems can achieve smooth interaction with flexible environments and obtain the desired tracking force without complex trajectory planning. The stability of the HGVF and the whole control scheme are analyzed by using the Lyapunov theorem. Finally, the effectiveness of the proposed method is validated through both simulation and experimental tests.

This paper presents an innovative event-triggered control approach for a class of nonlinear parabolic distributed parameter systems with time-varying delays. The novel event triggering mechanism enables control or measurement signals to be updated only when a predefined trigger condition exceeds a specified threshold. Multiple actuators and sensors, strategically distributed at specific points or partial regions of the spatial domain, are employed to perform pointwise/piecewise control and measurement. Two variations of event-triggered feedback (ETF) controllers are designed to address the collocated and non-collocated observation cases based on the distributions of actuators and sensors in space, respectively. The well-posedness of the open-loop and closed-loop systems is analyzed via the$C_{0}$-semigroup theory, respectively. Furthermore, the non-existence of zeno behavior is guaranteed by demonstrating that the inter-event time intervals are nontrivial. Finally, the proposed method is applied to address the temperature control problem in the catalytic reaction process. Numerical simulation results validate the effectiveness of the proposed ETF control method in practical applications. Note to Practitioners—This work is motivated by the temperature control challenges in catalytic reaction process, with an extended application to the production of hot-rolled steel strips. This paper proposes an innovative event-triggered control method to reduce the demands on communication and computational resources. Extensive comparative experimental results have thoroughly validated the effectiveness of the proposed method in practical applications.

When capturing scenes with intense light sources, extensive flare artifacts often obscure the background and degrade image quality. Most flare removal methods directly process the flare-corrupted image as the optimization target, limiting the model’s understanding and generalization in complex real-world scenarios. In this paper, we propose a novel Self-prior Guided Spatial and Fourier Transformer (SGSFT) for nighttime flare removal. Specifically, we first establish a Self-prior Extraction Network to capture inherent priors in different scenes. Subsequently, we introduce a Semantic Contrast Enhancement Strategy to reinforce the semantic irrelevance between flare and light source, enabling the flare removal network to learn pattern differences between them and thus preserve light source. Finally, we build a Spatial and Frequency Flare Removal Network with Spatial Contextual Attention Block (SCAB) and Frequency Global Information Adjustment Block (FGIAB) to generate flare-free image. SCAB can perceive rich contextual information from self-prior guided regions and infer reasonable content. FGIAB captures global luminance representation in the frequency domain to maintain luminance consistency between the inferred regions and the flare-free areas. Extensive experiments demonstrate that the proposed approach achieves optimal performance in real nighttime scenes and exhibits robust generalization across various flare scenarios captured by different electronic devices. Note to Practitioners—The motivation of this paper is to remove flare artifacts in imaging. Flares degrade image quality and impact the performance of advanced vision tasks such as semantic segmentation and depth estimation in autonomous driving. Existing methods that indiscriminately extract contextual information from the entire image limit the model’s understanding of flares. This study proposes a self-prior guided flare removal network. The network first extracts self-prior information from flare-damaged images, then aggregates non-local information from the context indicated by the self-prior information to remove flares and infer semantically plausible fill content. Additionally, we model the global luminance information of the image in the frequency domain to enhance the global luminance consistency of the flare-free image. Experimental results show that our method has strong flare removal capabilities, but it also has a limitation. The training phase of this method requires paired flare-damaged images and flare images, which are difficult to obtain in real-world scenarios. Therefore, we will explore unsupervised flare removal methods in the future.

This article investigates the trajectory tracking control of uncertain robotic systems with limited control torque input bounds and joint position constraints. A novel neural network-based augmented high-order control barrier function (NN-AHoCBF) is proposed to facilitate the tracking control strategy of uncertain robotic systems with input-output constraints, where the neural network (NN) is used to estimate uncertainties in the robotic system dynamics, and the bounds of NN approximation errors and NN weights are adapted in the high-order time derivative of the HoCBFs. The NN-AHoCBF is then derivated with a series of time-varying functions, and auxiliary systems are constructed to guarantee the time-varying functions to be HoCBFs. In this way, the control input of the robotic system is relaxed by adjusting the time-varying functions through the inputs of auxiliary systems in NN-AHoCBF barrier conditions. Also, the sufficient condition for the NN-AHoCBF is provided to adaptively ensure system safety. The adaptive safety-based tracking control method is designed based on NN-AHoCBF in quadratic program (QP) framework, which can not only satisfy input-output constraints simultaneously, but also achieve good robustness and tracking performance. A simulation example is performed on a two-DOF robotic mainpulator to verify the effectiveness of the developed controller.

As an efficient recurrent neural network (RNN), reservoir computing (RC) has achieved various applications in time-series forecasting. Nevertheless, a poorly explained phenomenon remains as to why the RC and deep RCs succeed in handling time-series prediction despite completely randomized weights. This study tries to generate a grouped vector autoregressive RC (GVARC) time-series forecasting model based on the randomly distributed embedding (RDE) theory. In RDE-GVARC, the deep structures are constructed by multiple GVARCs, which makes the established RDE-GVARC evolve into a deterministic deep RC model with few hyperparameters. Then, the spatial output information of the GVARC is mapped into the future temporal states of an output variable based on RDE equations. The main advantages of the RDE-GVARC can be summarized as follows: 1) RDE-GVARC solves the problems of uncertainty in the weight matrix and difficulty in large-scale parameter selection in the input and hidden layers of deep RCs; 2) the GVARC can avoid massive deep RC hyperparameter design and make the design of deep RC more straightforward and effective; and 3) the proposed RDE-GVARC shows good performance, strong stability, and robustness in several chaotic and real-world sequences for multistep-ahead prediction. The simulating results confirm that the RDE-GVARC not only outperforms some recently deep RCs and RNNs, but also maintains the rapidity of RC with an interpretable structure.

Evolutionary multitask optimization (EMTO) can solve multiple tasks simultaneously by leveraging the relevant information between tasks, but existing EMTO algorithms do not take into account the fact that almost all problems in the real world contain constraints. To address this dilemma, this article studies a local knowledge transfer-based evolutionary algorithm for constrained multitask optimization. To be specific, each task population is divided into multiple niches to enhance the diversity and control the intensity of knowledge transfer, thus avoiding excessive transfer of knowledge. Then a new similarity judgment method based on the information feedback of pioneer individuals is developed to judge the similarity between tasks and whether to perform knowledge transfer. Furthermore, two different transfer methods: a direct transfer and a learning transfer, are devised to perform knowledge transfer among niches pertaining to different tasks. In addition, an excellent-information-guided mutation mechanism is proposed to prevent niches from getting trapped in local optima and to promote rapid convergence. The system experiment on 18 constrained multitask test instances and 2 real-world problems demonstrate that the proposed algorithm outperforms or is at least comparable to other EMTO algorithms and constrained single-objective optimization algorithms.

In this article, we propose a robust high-order control barrier functions (HoCBFs)-based optimal control method for nonlinear systems with state constraints to achieve safety-stability perspectives. First, a kind of HoCBFs is presented for constrained nonlinear systems to address state constraints with high relative degrees. Second, the robustness property of the HoCBFs is analyzed based on the asymptotic stability of the forward invariant set. Specifically, a robust HoCBFs-based Lyapunov function is constructed to prove the uniform asymptotic stability of the set associated with the HoCBFs. In this way, a new sufficient condition is obtained for the stability analysis of the forward invariant set by using the inequalities of high-order derivatives of Lyapunov function. Third, a robust HoCBFs-based optimal control scheme is proposed for the constrained nonlinear system to achieve the safety-stability perspectives of constraints satisfaction and system stabilization, where the robust HoCBFs are combined with control Lyapunov functions (CLFs) to satisfy the small control property (SCP) in solving a quadratic program (QP). Furthermore, the proposed optimal control scheme is shown to be Lipschitz continuous and has no initial condition restrictions. Finally, two examples are presented to demonstrate the control performance of the proposed scheme.Note to Practitioners—The motivation of this article is that constraints exist widely in actual control systems, and the lack of constraint satisfaction in control systems may inevitably lead to safety defects, which usually degrade the control performances or even damage the entire system. In this article, a robust HoCBFs-based optimal control scheme is proposed for constrained nonlinear systems. The theoretical derivation demonstrates that the proposed control scheme can achieve safety-stability perspectives, which ensure system stabilization and task-oriented performance without violating the state constraints. The satisfactory control performances of the simulation on a constrained robotic manipulator show the potential practical application on a real robotic system.

In this article, we investigate the problem of security control for T-S fuzzy Markov jump systems (FMJSs) under actuator faults and deception attacks, while introducing a cooperative event-triggered mechanism (CETM). In order to enhance the efficiency of communication resources, we develop the CETM in the forward channel, which operates concurrently on sensor-to-observer (STO) and observer-to-controller (OTC) channels utilizing a united event generator. Additionally, we design an attack-compensating controller to eliminate the impact of nonlinear malicious injection information generated by deceptive attacks on the system, where the compensation signal is generated by approximating the attack signal using radial basis function neural network (RBFNN) technology. Furthermore, using the Lyapunov function, sufficient conditions for ensuring that T-S FMJSs are mean square exponential ultimate bounded (MSEUB) are derived. Finally, the effectiveness of our proposed approach is demonstrated through a simulation example.

Infrared small target detection plays a crucial role in various domains like early warning, national defense, and monitoring. Although existing detection methods have achieved some good results, they only rely on the original size and information of small targets for detection and are faced with challenges, such as the small size and obscure feature information of small targets. To overcome these limitations, this article introduces a coarse-to-fine detection network named MSMA-Net. This network initially determines the rough location of targets through a coarse preliminary screening, aiming to reduce false alarms and improve computational efficiency. Simultaneously, to improve the discriminability of the features and enhance the spatial details and resolution of the targets, the network utilizes multiscale super-resolution to transform low-resolution feature maps into high-resolution representations, gradually refining and strengthening the feature representation. Finally, the network employs a multilevel feature fusion attention mechanism to facilitate effective information transmission and fusion in multiscale and multilevel feature representations. This attention mechanism enhances the accuracy as well as robustness of object detection, ultimately obtaining accurate detection results. Extensive experimental results demonstrate that compared with existing detection methods, our approach can effectively suppress false alarms and get better performance even when the target has a small size and obscure feature information.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

This paper investigates a novel routing problem of a multi-robot station in a manufacturing cell. In the existing literature, the objective is to minimize the cycle time or energy consumption separately. The routing problem considered in this paper aims to reduce the cycle time and energy consumption jointly for each robot while avoiding collisions between these robots. For this routing problem, we propose a new flexible time-space network model that allows us to reduce energy consumption while minimizing the cycle time. The corresponding optimization problem is Mixed-Integer Nonlinear Programming (MINLP). For addressing its computational complexity, this paper designs a metaheuristic algorithm tailored to the studied problem and proposes an$\varepsilon $-constraint algorithm to study the trade-off between these two objectives. We conduct industrially relevant simulation experiments of case studies to show its effectiveness, in comparison to a conventional method, two state-of-the-art solvers, and two commonly-used metaheuristics. The results show that the proposed methodology can reduce energy consumption by up to 30% without compromising the cycle time. Meanwhile, the proposed algorithm can provide efficient solutions within a reasonable computation time.Note to Practitioners—This paper is motivated by the problem of improving energy efficiency when routing cooperative robots in a manufacturing station. In current approaches for routing multi-robot stations, the cycle time and energy consumption are minimized separately. This paper focuses on the movement of the robot end-effector and its connected joint and suggests a new approach to minimize these two objectives jointly by proposing a new mathematical model. The resulting planning problem is computationally intractable. A customized metaheuristic algorithm is thus designed for efficiently solving this planning problem. Our meta-heuristic algorithm is integrated with the$\varepsilon $-constraint method to study the relationship between these two objectives. Simulation experiments suggest that this approach can reduce energy consumption considerably, for the shortest cycle time, compared with the current approaches. In future research, the movements of multi-joints will be investigated whereby 3-D collision-free trajectory planning will be considered.

In this article, we propose a composite control framework for time-varying nonlinear multiple-input–multiple-output (MIMO) systems with safety constraints and unknown dynamics. The control framework combines control barrier functions (CBFs) and high-order CBFs (HoCBFs) with an extended state observer (ESO) to handle arbitrary relative-degree constraints in the presence of system uncertainties and output measurements only. Then, the ESO-CBF/HoCBF-based safety control schemes are obtained by solving quadratic programs (QPs) to ensure the safety of closed-loop control systems. Consequently, the safety satisfaction and uncertainties compensation objectives can be achieved simultaneously. Finally, simulations and experiments are conducted to verify the effectiveness of the proposed safety control schemes.

This article investigates a new collision-free routing problem of a multirobot system. The objective is to minimize the cycle time of operation tasks for each robot while avoiding collisions. The focus is set on the operation of the end-effector and its connected joint, and the operation is projected onto a circular area on the plane. We propose to employ a time-space network (TSN) model that maps the robot location constraints into the route planning framework, leading to a mixed integer programming (MIP) problem. A dedicated genetic algorithm is proposed for solving this MIP problem and a new encoding scheme is designed to fit the TSN formulation. Simulation experiments indicate that the proposed model can obtain the collision-free route of the considered multirobot system. Simulation results also show that the proposed genetic algorithm can provide fast and high-quality solutions, compared to two state-of-the-art commercial solvers and a practical approach.