This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints. Unlike the constraints considered in most existing papers, here the external irregular constraints are considered and a constraints switching mechanism (CSM) is introduced to circumvent the difficulties arising from irregular output constraints. Based on the CSM, a new class of generalized barrier functions are constructed, which allows the control results to be independent of the maximum and minimum values (MMVs) of constraints and thus extends the existing results. Finally, we proposed a novel dynamic constraint-driven event-triggered strategy (DCDETS), under which the stress on signal transmission is reduced greatly and no constraints are violated by making a dynamic trade-off among system state, external constraints, and inter-execution intervals. It is proved that the system output is driven to close to the reference trajectory and the semi-global stability is guaranteed under the proposed control scheme, regardless of the external irregular output constraints. Simulation also verifies the effectiveness and benefits of the proposed method.

The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry. The copper disc casting machine casting package motion curve (CPMC) is significant for precise casting and efficient production. However, the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization. To this end, a liquid copper droplet model describes the casting package copper flow pattern in the casting process. Furthermore, a CPMC optimization model is proposed for the first time. On top of this, a digital twin dual closed-loop self-optimization application framework (DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process. Finally, a case study is carried out based on the proposed methods in the industrial field.

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems (SPSs) with unknown dynamics based on reinforcement learning (RL). Taking into account the slow and fast characteristics among system states, the interconnected SPS is decomposed into the slow time-scale dynamics and the fast time-scale dynamics through singular perturbation theory. For the fast time-scale dynamics with interconnections, we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function. For the slow time-scale dynamics with unknown system parameters, an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data. By combining the slow and fast controllers, we establish the composite decentralized adaptive optimal output regulator, and rigorously analyze the stability and optimality of the closed-loop system. The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality. The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

This paper presents a novel sequential inverse optimal control (SIOC) method for discrete-time systems, which calculates the unknown weight vectors of the cost function in real time using the input and output of an optimally controlled discrete-time system. The proposed method overcomes the limitations of previous approaches by eliminating the need for the invertible Jacobian assumption. It calculates the possible-solution spaces and their intersections sequentially until the dimension of the intersection space decreases to one. The remaining one-dimensional vector of the possible-solution space’s intersection represents the SIOC solution. The paper presents clear conditions for convergence and addresses the issue of noisy data by clarifying the conditions for the singular values of the matrices that relate to the possible-solution space. The effectiveness of the proposed method is demonstrated through simulation results.

Low-light images suffer from low quality due to poor lighting conditions, noise pollution, and improper settings of cameras. To enhance low-light images, most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult. In contrast, a self-supervised method breaks free from the reliance on normal-light data, resulting in more convenience and better generalization. Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods, resulting in remnants of other degradations, uneven brightness and artifacts. In response, this paper proposes a self-supervised enhancement method, termed as SLIE. It can handle multiple degradations including illumination attenuation, noise pollution, and color shift, all in a self-supervised manner. Illumination attenuation is estimated based on physical principles and local neighborhood information. The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts. Finally, the comprehensive and fully self-supervised approach can achieve better adaptability and generalization. It is applicable to various low light conditions, and can reproduce the original color of scenes in natural light. Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods. Our code is available at

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The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks, utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism (DETM). In order to optimize communication resource utilization, the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not. To guarantee a satisfactory estimation performance for the fault signal, an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals. The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains. The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator, ensuring that the specified performance requirements are met under certain conditions. Then, the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided. Finally, a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

This paper investigates the consensus control of multi-agent systems (MASs) with constrained input using the dynamic event-triggered mechanism (ETM). Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed. Then, a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks. It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time. In addition, it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter. Furthermore, the results are extended to regional consensus of the MASs with the bounded control protocol. Numerical simulations show the effectiveness of the proposed approach.

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators. First, a fixed-time disturbance observer (FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems. Then an adaptive scheme is used and the adaptive FTDO (AFTDO) is developed, so that the priori knowledge of the lumped disturbance is not required. Further, a new non-singular fast terminal sliding mode (NFTSM) surface is designed by using an arctan function, which helps to overcome the singularity problem and enhance the robustness of the system. Based on the estimation of the lumped disturbance by the AFTDO, a fixed-time non-singular fast terminal sliding mode controller (FTNFTSMC) is developed to guarantee the trajectory tracking errors converge to zero within a fixed time. The settling time is independent of the initial state of the system. In addition, the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method. Finally, the fixed-time NFESM (FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers (SMCs) are performed. The comparative results confirm that the FTNFTSMC has superior control performance.

Monocular 3D object detection is challenging due to the lack of accurate depth information. Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images. Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning. However, they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions. Different from these approaches, our proposed depth-guided vision transformer with a normalizing flows (NF-DVT) network uses normalizing flows to build priors in depth maps to achieve more accurate depth information. Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other. Furthermore, with the help of pixel-wise relative depth values in depth maps, we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens. Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection. The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

This paper presents a novel cooperative value iteration (VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof. The players are divided into two groups in the learning process and adapt their policies sequentially. Our method removes the dependence of admissible initial policies, which is one of the main drawbacks of the PI-based frameworks. Furthermore, this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws. The efficacy of our method is illustrated by three examples.

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images. Current methods feed the whole image directly into the model for enhancement. However, they ignored that the R, G and B channels of underwater degraded images present varied degrees of degradation, due to the selective absorption for the light. To address this issue, we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel. Specifically, an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images. Based on this, we design a generator, including a multi-expert encoder, a feature fusion module and a feature fusion-guided decoder, to generate the clear underwater image. Accordingly, a multi-expert discriminator is proposed to verify the authenticity of the R, G and B channels, respectively. In addition, content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images. Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality. Various metrics (PSNR, SSIM, UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

This article focuses on dynamic event-triggered mechanism (DETM)-based model predictive control (MPC) for T-S fuzzy systems. A hybrid dynamic variables-dependent DETM is carefully devised, which includes a multiplicative dynamic variable and an additive dynamic variable. The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem (OP). To facilitate the co-design of the MPC controller and the weighting matrix of the DETM, an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant (RPI) set that contain the membership functions and the hybrid dynamic variables. A dynamic event-triggered fuzzy MPC algorithm is developed accordingly, whose recursive feasibility is analysed by employing the RPI set. With the designed controller, the involved fuzzy system is ensured to be asymptotically stable. Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

In this paper, distributed model predictive control (DMPC) for island DC micro-grids (MG) with wind/photovoltaic (PV)/battery power is proposed, which coordinates all distributed generations (DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement. Based on the feedback of the bus voltage, the deviation of the current is dispatched to each DG according to cost over the prediction horizon. Moreover, to avoid the excessive fluctuation of the battery power, both the discharge-charge switching times and costs are considered in the model predictive control (MPC) optimization problems. A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire system. The voltage stabilization of the MG is achieved by this strategy with the cooperation of DGs. The numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

This paper considers a linear-quadratic (LQ) mean-field game governed by a forward-backward stochastic system with partial observation and common noise, where a coupling structure enters state equations, cost functionals and observation equations. Firstly, to reduce the complexity of solving the mean-field game, a limiting control problem is introduced. By virtue of the decomposition approach, an admissible control set is proposed. Applying a filter technique and dimensional-expansion technique, a decentralized control strategy and a consistency condition system are derived, and the related solvability is also addressed. Secondly, we discuss an approximate Nash equilibrium property of the decentralized control strategy. Finally, we work out a financial problem with some numerical simulations.

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties, external disturbances and input deadzone. Considering the model complexity and input deadzone, a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design. Firstly, the control gain of ultra-local model is considered as a constant. The fractional-order sliding mode technique is designed to stabilize the closed-loop system, while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance. Correspondingly, a fractional-order ultra-local model-based neural network sliding mode controller (FO-NNSMC) is proposed. Secondly, to avoid disadvantageous effect of improper gain selection on the control performance, the control gain of ultra-local model is considered as an unknown parameter. Then, the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain. Correspondingly, a fractional-order ultra-local model-based adaptive neural network sliding mode controller (FO-ANNSMC) is proposed. Moreover, the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory. Finally, with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton, the obtained compared results illustrate the effectiveness and superiority of the proposed method.