This article develops a dual-link coded event-triggered control (DL-CEC) for consensus in nonlinear multiagent system. To reduce the communication burden of signal transmission between the control box and actuator box or among agents and to enhance the security of information exchange, a dual-link coded scheme is proposed to compress each transmitted information into an L-length string. Furthermore, since the intrinsic complexity of nonlinear systems often causes traditional prescribed performance methods to fail in meeting constraints during the initial stages of operation, an adaptive prescribed performance (APP) scheme is introduced. By utilizing auxiliary functions, the APP is capable of dynamically adjusting performance boundaries, enabling seamless adaptation to varying initial system conditions. As a result, it ensures the tracking error is rigorously guaranteed to remain within a user-defined range over a prescribed time horizon, effectively accommodating diverse initial conditions of the system. By integrating DL-CEC with the APP method, the proposed control strategy ensures bounded consensus tracking with reduced communication cost and prescribed-time performance under arbitrary initial conditions. Simulation experiments corroborate the effectiveness and feasibility of the proposed approach.

Adversarial attack adds an imperceptible perturbation on images to fool a model. Though existing adversarial attack methods have demonstrated great success in image classification tasks, they suffer inferior attack performances on object detection. We find that there exists multi-target gradient conflict (MGC) between different targets during attack process in object detection. To tackle this issue, we propose an effective pixel-level attack method, namely Selective Consistency Gradient Attack (SCGA). First, we select a dominant gradient direction by gradient ranking. Then, we introduce the gradient conflict rate to select targets with consistent gradients to improve attack efficiency. Finally, the perturbation is generated by gradient merging. Experiments on COCO 2017 validation subset verify the effectiveness of SCGA on both white-box attack and black-box attack, outperforming other methods with a large margin.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

In the realm of multi-agent systems, the effective coordination of agents for manipulation tasks poses a significant challenge. This study explores various strategies aimed at enhancing Multi-agent Reinforcement Learning (MARL) algorithms in the context of locomotion-based manipulation tasks. We systematically assess the performance of these strategies, incorporating reward shaping and algorithmic variations such as Proximal Policy Optimization (PPO) and Advantage Actor Critic (A2C). For better cooperation between agents, a prediction map is also implemented, informing the agents with a probability heat map of other agents based on their kinematic models. The experiments are conducted in the Isaac Sim simulation environment with Jetbot robots as agents. Results indicate distinct impacts of each strategy on critical performance metrics, including success rates, collision probabilities, and overall task efficiency. While some strategies exhibit notable improvements, others reveal limitations, emphasizing the nuanced challenges inherent in optimizing multi-agent systems for this task. These findings serve as an overview of current Reinforcement Learning (RL) optimization strategies, and could contribute valuable insights to the effective deployment of RL in complex, collaborative robotic scenarios.

This article investigates an output rate-coded secure control based on backstepping for the consensus of heterogeneous multiagent systems (MASs) with output-triggering condition. Due to the nondifferentiable virtual control inputs caused by discontinuous triggering signals, it presents a technical obstacle in implementing the recursive backstepping, rendering previous results inapplicable. To address this problem, an auxiliary high-order filter is elaborately constructed to guarantee the required-order derivatives of virtual control inputs. As this filter is driven by local triggering consensus error, it allows our MASs to have different system orders and relative degrees. Besides, to reduce communication bit and strengthen communication security, we propose a novel distributed rate-coded algorithm from an encoding-decoding viewpoint. When it is triggered, agent's output is encrypted into an L-length codeword, then transmitted to neighbors without exposing any sensitive system states or real control inputs. It is shown that all the closed-loop system signals are ultimately bounded, and the mean square consensus tracking error can be reduced by appropriately selecting control parameters. Simulations illustrate our theoretical finding.

In this article, saturation-tolerant prescribed control (SPC) is investigated for a class of multiinput-multioutput (MIMO) nonlinear systems. The key challenge lies in how to guarantee both input and performance constraints simultaneously for nonlinear systems especially under external disturbance and unknown control directions. We propose concise finite-time tunnel prescribed performance (FTPP) for better tracking performance, which features tight allowable set and user-specified settling time. To comprehensively tackle the conflict between the above two constraints, an auxiliary system is designed to explore their interconnections instead of neglecting their contradictions. By introducing its generated signals into FTPP, the obtained saturation-tolerant prescribed performance (SPP) has the ability to degrade or recover the performance boundaries in the light of different saturation conditions. Consequently, the developed SPC, together with nonlinear disturbance observer (NDO), can effectively improve the robustness and reduce the conservatism against external disturbances, input, and performance constraints. Finally, comparative simulations are presented to showcase these theoretical findings.

This article studies an event-triggered tunnel prescribed control (TPC) for uncertain nonlinear systems under any initial condition. A more general entry capture problem (ECP) is introduced, where the tunnel prescribed performance is satisfied after a certain period of system operation, as opposed to starting from the beginning, thereby, making the control design complex yet challenging. In this case, the normally employed prescribed performance control becomes invalid due to the singularity problem arising from the initial condition violation. An error self-tuning function is proposed to provide a unified approach for handling different initial conditions, which can be extended to other methods. In order to deal with unknown control directions, an orientation function is employed in lieu of Nussbaum-type function, by which the initial control input is always equal to zero avoiding a large initial value. We then develop an event-triggered mechanism from an encoding–decoding viewpoint, by which only 1-bit string, either 1 or 0, is required for each communication between control and actuator. In this way, such event-triggered mechanism can further reduce communication burden and improve communication security at the same time. The developed event-triggered TPC provides an effective solution for the underlying ECP and exhibits low-complexity level since no additional filters or time derivatives of virtual control inputs are required in the control design. Finally, comparative simulations are conducted to illustrate the abovementioned theoretical finding.

This paper is concerned with a performance-prescribed coordinated control problem of multiple underactuated marine surface vehicles (MSVs) subject to internal uncertainties, external disturbances, and actuator faults. An echo state network-based (ESN-based) transient-reinforced tunnel coordinated control method is proposed for underactuated MSVs with prescribed performance metrics. Specifically, a graph-based trajectory generator is designed to generate reference signals for various application scenarios. In the guidance loop, a tunnel prescribed performance (TPP) is established to characterize the position and heading coordination metrics of underactuated MSVs. With the TPP-based equivalent transformation, the tunnel guidance laws are devised by an underactuation guidance principle. In the control loop, an ESN-based neural estimator is constructed to identify unknown kinetics consisting of internal uncertainties, external disturbances, and actuator faults. Utilizing the estimated information, the ESN-based surge and yaw control laws are presented. The proposed closed-loop system is proven to be input-to-state stable via the theoretical analysis, and position and heading tracking errors can evolve within TPP constraints regardless of actuator faults. Finally, comparison simulation results are employed to verify the effectiveness and superiority of the proposed method.

This article addresses the entry capture problem (ECP) of uncertain nonlinear systems under asymmetric performance constraints. We show that such ECP is commonly encountered in practice that has not been well addressed, whose tracking error is free from any performance constraints initially then is driven into the prescribed region in finite time. For better-transient performance, a unified tunnel prescribed performance (TPP) is developed to provide strict and tight allowable set. By utilizing a scaling function, together with an error scaling function (ESF), and a more general error-dependent transformation function (ETF), we propose an event-triggered tracking control strategy leading to a solution for the underlying ECP with various initial conditions and asymmetric performance constraints. This control strategy is of significant simplicity, stemming from that only 1-bit signal is needed for each data transmission between controller and actuator. We also show that the tracking error (including the initial-constraint violation) is regulated into the prescribed region in a given time globally. Finally, simulations are conducted to illustrate the above theoretical findings.

The classical optimal control of the linear system assumes that the system is stabilizable, thereby deriving the optimal control with the outcome that the solution inherently stabilizes the system. Such optimization does not distinctly address stabilization and optimization as separate concerns, leading to a situation where, as the system expands in size and complexity, the optimal controller suffers performance decreases and becomes increasingly sensitive and fragile. In this article, nested optimized control (NOC) and nested optimized adaptive control (NOAC) are introduced to explicitly handle the stabilization, optimization/adaptation for unknown parameters separately in an effort to strike a balance between guaranteed stability and optimal control. The robustness of the classical optimal control is inherent in the design itself, and the stability margin is relatively small subject to parameter uncertainties. In our NOC, the robustness is explicitly handled by the state feedback control and its stability margin is larger than the classical one, because of the introduction of the explicit state feedback control loop, the next optimized control loop is introduced for system performance. Note that, the term optimized rather than optimal is used here as it is not the classical optimal control anymore, but a fundamental change in design methodology. To further improve the stability margin due to parameter uncertainties, adaptive control is introduced to approximate the parameters in an effort to further improve the stability margin. The effectiveness of the proposed method is demonstrated through comparative examples that highlight its advantages.

This article studies the problem of saturation-tolerant prescribed control (SPC) for a class of nonlinear time-delay systems with unknown control directions. We propose a unified finite-time tunnel prescribed performance (FTPP), which not only provides more tight allowable set leading to smaller overshoot, but also drives the tracking error into the prescribed set within a known time. To remove the implicit assumption that both input and performance constraints need to be satisfied simultaneously, an auxiliary system is developed to establish a balance between these two constraints instead of ignoring their interconnection and conflict. With aid of its generated nonnegative signals, the developed saturation-tolerant prescribed performance (SPP) possesses flexible performance. Namely, SPP can temporarily enlarge the performance boundaries to guarantee both constraints when input saturation occurs, and fast recover back to the prescribed boundaries when input saturation disappears. Consequently, a low-complexity SPC for uncertain nonlinear systems is developed, which can always guarantee both input and performance constraints even under unknown time delay and unknown control directions. Finally, comparative simulation is provided to illustrate the merits of the presented control strategy.

Aiming at the problem that how to design a controller without using model information for an unmanned helicopter (UH), a novel data-driven method based on model free adaptive control (MFAC) is proposed in this article. A new form of dynamic linearization (DL) equation composed of pseudo-partial derivative (PPD) and external disturbance is built in order to reduce the influence of disturbance on PPD. Since there are internal and external unknowns included in the DL equation, a nominal DL equation without external disturbance item is introduced to make the estimation of the two items decoupled. The estimation algorithm of PPD is designed based on the nominal DL equation, and a discrete extended state observer is applied specifically into the estimation of external disturbance based on the original DL equation. Besides, with the nominal DL equation, a modified MFAC scheme called PID-like MFAC, which considers the historical input and output (I/O) data is given to make a better performance and a more convenient application compared with the typical MFAC scheme. Furthermore, the stability of the control scheme is proved. Finally, comparative simulations are carried out to verify the effectiveness of the proposed control scheme. An experiment based on UH also verifies its practical performance and the theoretical findings.

This article proposes a saturation-tolerant prescribed control (SPC) for a class of multiinput and multioutput (MIMO) nonlinear systems simultaneously considering user-specified performance, unmeasurable system states, and actuator faults. To simplify the control design and decrease the conservatism, tunnel prescribed performance (TPP) is proposed not only with concise form but also smaller overshoot performance. By introducing non-negative modified signals into TPP as saturation-tolerant prescribed performance (SPP), we propose SPC to guarantee tracking errors not to violate SPP constraints despite the existence of saturation and actuator faults. Namely, SPP possesses the ability of enlarging or recovering the performance boundaries flexibly when saturations occur or disappear with the help of these non-negative signals. A novel auxiliary system is then constructed for these signals, which bridges the associations between input saturation errors and performance constraints. Considering nonlinearities and uncertainties in systems, a fuzzy state observer is utilized to approximate the unmeasurable system states under saturations and unknown actuator faults. Dynamic surface control is employed to avoid tedious computations incurred by the backstepping procedures. Furthermore, the closed-loop state errors are guaranteed to a small neighborhood around the equilibrium in finite time and evolved within SPP constraints although input saturations and actuator faults occur. Finally, comparative simulations are presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

In this article, we investigate the saturation-tolerant prescribed control (SPC) for multiinput and multioutput nonlinear systems with unknown control directions and actuator faults. We propose a concise tunnel prescribed performance (TPP) with the control design independent of initial conditions and smaller overshoots achieved due to its tight feasible region. A novel auxiliary system, to tactfully establish a feedback mechanism between input saturation and prescribed performance, is constructed. By introducing the generated nonnegative modifications into the TPP, the resulted saturation-tolerant prescribed performance (SPP) is capable of flexibly degrading performance constraints in the case of saturation; and recovering back to the user-specified performance in the case without saturation. Furthermore, the proposed control scheme guarantees not only finite-time convergence, but also SPP-constrained tracking performance despite the input saturation and uncertainties. Finally, comparative results are provided to demonstrate the distinctive merit of the proposed SPC more than the traditional prescribed performance control.

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

This article addresses the finite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Furthermore, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in finite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.