This paper addresses the intricate control challenges posed by unknown Euler–Lagrange systems operating under actuator faults and subject to asymmetric error constraints. A novel prescribed performance control (PPC) strategy is proposed, leveraging a dual-error transformation technique. The first transformation is designed to decouple the initial tracking errors from the predefined performance functions, thereby significantly relaxing the stringent initial condition dependencies typical of conventional PPC schemes and ensuring errors remain confined within adjustable asymmetric boundaries. The second transformation introduces exponential decay constraint functions to dynamically regulate the convergence rate and steady-state accuracy of the tracking errors. Theoretical analysis rigorously demonstrates that the proposed strategy, without requiring prior knowledge of the system’s complex nonlinearities, guarantees global boundedness of all closed-loop signals. Furthermore, it ensures that tracking errors converge to prespecified asymmetric residual zones at a preset rate, even in the presence of actuator faults. The efficacy and superiority of the proposed strategy are validated through comparative simulations conducted on a two-link robotic manipulator. The experimental source code is available at https:// github.com/hclll22/Dual-Error-Transformation-Approach-PPC.

Significant theoretical and practical challenges arise in the cooperative control of distributed nonlinear multi-agent systems (MAS), particularly when they involve nonstrict-feedback interconnections and unknown state-dependent control gains. Conventional neural adaptive controllers, while versatile, often operate as “black-box” models, leading to solutions that may lack physical plausibility and exhibit compromised robustness. This paper addresses this critical gap by introducing a novel Physics-Regularized Adaptive Control (PRAC) framework, implemented via neural backstepping. Central to PRAC is the design of novel Physics-Regularized Neural Networks (PRNNs), which are realized using Radial Basis Function Neural Networks (RBFNNs) as their architectural foundation in this paper. Instead of treating the neural network as a simple approximator, the PRAC methodology embeds physical priors, such as equilibrium conditions, system smoothness, and energy dissipation principles, into the PRNNs’ online adaptive laws as differentiable regularization terms. The gradients of these terms actively constrain the PRNN weight adaptation, transforming the learning process into a Lyapunov-guided constrained optimization. This enhances the physical consistency and interpretability of the learned dynamics while simultaneously improving control performance. By synergistically combining this physics-regularized architecture with Dynamic Surface Control (DSC) to manage computational complexity, the proposed scheme guarantees cooperative uniformly ultimately bounded (CUUB) tracking of a leader’s trajectory. Rigorous Lyapunov analysis substantiates the theoretical guarantees, which are further validated by comprehensive numerical simulations and a practical networked inverted-pendulum example demonstrating superior tracking accuracy and robustness over conventional neural adaptive controllers.

This article introduces a novel fuzzy logic-enhanced neuroadaptive sliding mode control (FLENNSMC) framework, developed for vehicular platoon systems subject to a confluence of challenges. Leveraging the synergistic integration of fuzzy logic's interpretive strengths and neural networks' adaptive learning capabilities, FLENNSMC effectively addresses nonlinear dynamics, stochastic disturbances, actuator faults, and stringent asymmetric spacing constraints. We propose a Takagi-Sugeno (T-S) fuzzy model to structure the learning process and a fuzzy logic-enhanced RBFNN (FLERBFNN) for robust approximation of unknown functions, including unmodeled dynamics and fault signals. The controller design incorporates a fault-tolerant control mechanism for enhanced robustness, an asymmetric barrier Lyapunov function (BLF) to strictly enforce spacing constraints, and a Nussbaum function to compensate for actuator faults with unknown directions. The fuzzy logic-enhanced structure allows for localized and efficient learning, which reduces computational burden and improves adaptation speed. Through a rigorous stochastic Lyapunov-Krasovskii stability analysis, we derive sufficient LMI-based conditions for the uniform ultimate boundedness (UUB) of tracking errors in the mean square sense and guarantee a mixed H-infinity/passivity performance. Extensive simulations on a 2-D multilane vehicular platoon demonstrate the superior performance of the proposed FLENNSFC compared to conventional neuroadaptive control approaches, particularly highlighting the benefits of fuzzy logic in structuring the learning process and handling complex uncertainties. Simulation code is available at https://github.com/zhanganguo/FLENNSMC-Platoon-Control-Simulation.

This article investigates the reliable navigation problem, which requires that the ego vehicle navigates itself to the destination with the maximized stochastic on-time arrival (SOTA) probability in a givenuncertaintopological transportation network. One distinctive characteristic of the SOTA problem explored in this paper is the inherent uncertainty stemming from the underlying network's topology, i.e., some of the edges might become untraversable during navigation. To the best of our knowledge, almost all conventional SOTA solutions presume that the network topology remains the same during the ego vehicle's navigation process. However, when facing uncertainties in the network's topology, these algorithms may experience a significant performance degradation. To address the challenge of uncertain network topology, we first formulate the special reliable navigation problem into thevariationalMarkov decision process (MDP) framework, and then initiate a new reinforcement learning (RL)-based algorithm, namely sample efficient variational actor critic (SEVAC) as its solution. SEVAC comprises the variational policy gradient (VPG) module, which optimizes the vehicle's routing policy, and the masked temporal difference (MTD) module, which approximates the underlying routing policy's SOTA probability. Both modules are extended to their off-policy counterparts, namely off-policy VPG and off-policy MTD, to improve the algorithm's sample efficiency. SEVAC is compared with several conventional SOTA solutions as well as Canadian traveller problem (CTP) algorithms in a variety of commonly used transportation test networks, and achieves the best overall SOTA performance. Furthermore, we validate SEVAC's application to real-world scenarios by navigating a physical robot in a self-constructed indoor environment as well as a real-world building environment with uncertain topology.

This paper studies secure consensus of nonlinear multi-agent systems (MASs) affected by sequential scaling attacks and communication delays, employing an event-triggered delayed impulsive control strategy. Specifically, it considers sequential scaling attacks occurring within the communication channels between agents, while the communication delays arise in the controller-actuator pair. First, the attack properties include attack duration and attack frequency are defined. Then, a delayed impulsive control protocol that depends exclusively on neighboring agents’ state at event-triggered time instant is proposed to eliminate continuous control behavior. A sampled-data-based event-triggered mechanism (ETM) is introduced that uses the Lyapunov function at impulse time instant to determine communication intervals between agents, effectively reducing the need for continuous event detection. Furthermore, sufficient conditions for secure consensus of MASs are established, along with guidelines for designing event-triggering parameters. Finally, the effectiveness of the proposed approach is demonstrated via two numerical simulations.

With the gradual application of reinforcement learning (RL), safety has emerged as a paramount concern. This article presents a novel data-model hybrid-driven safe RL (SRL) scheme to address the challenge of avoidance control in the operation domain containing multiple moving unsafe zones. First, the avoidance problem is transformed into the optimal control problem of an augmented system by encoding a barrier function (BF) term into the cost function. Then, using the idea of integral RL (IRL), an adaptive learning algorithm is proposed for generating safe control policies, in which the actor-critic neural network (NN) structure is established with the aid of state-following (StaF) kernel function. The policy iteration process is executed by this structure; specifically, the critic network undergoes gradient-descent adaptation, while the actor network employs gradient projection updating. Particularly, via a state extrapolation technique, both real-time experience and simulated experience are utilized in the learning process. Next, closed-loop stability and weight convergence are theoretically substantiated. Finally, the effectiveness of the proposed scheme is demonstrated on a single integrator system, a nonlinear numerical system, and a unicycle kinematic system; besides, its advantages over the existing control methods are illustrated by comparisons.

In this article, we proposed a novel fault-tolerant control scheme for quadrotor unmanned aerial vehicles (UAVs) based on spiking neural networks (SNNs), which leverages the inherent features of neural network computing to significantly enhance the reliability and robustness of UAV flight control. Traditional control methods are known to be inadequate in dealing with complex and real-time sensor data, which results in poor performance and reduced robustness in fault-tolerant control. In contrast, the temporal processing, parallelism, and nonlinear capacity of SNNs enable the fault-tolerant control scheme to process vast amounts of sensory data with the ability to accurately identify and respond to faults. Furthermore, SNNs can learn and adjust to new environments and fault conditions, providing effective and adaptive flight control. The proposed SNN-based fault-tolerant control scheme demonstrates significant improvements in control accuracy and robustness compared with conventional methods, indicating its potential applicability and suitability for a range of UAV flight control scenarios.

In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.

Long-term (also called Clothing-Change) person re-identification (CC-reID) aims at confirming the identity of pedestrians captured at diverse locations and/or times. Current CC-reID methods heavily rely on ID features learned by the CNN architecture. However, with limited receptive fields, CNN is hard to effectively explore some unique but discriminative ID features (e.g., hair style, tattoo and accessories) from small body regions. Compared with CNN, Transformer has certain merits in exploring more diverse ID-unique features1and retaining more details by the multi-head self-attention design and the removal of down-sampling operation. In this paper, a two-stream hybrid Convolution-Transformer Network (CT-Net) is proposed for CC-reID by combining both CNN and Transformer parallelly in an end-to-end learning scheme. Specifically, CT-Net contains a CNN-based stream (C-Stream) and a Transformer-based stream (T-Stream). Compared with using C-Stream only, T-Stream is used to encourage the C-Stream to explore more detailed ID-unique features when the clothing information is no reliable in CC-reID. Specifically, a Feature Supplement Module (FSM) is proposed to transfer features learned by T-Stream to C-Stream from low-level to high-level for mining more ID-unique feature. In order to further enhance the discriminability2and complementary of ID features learned by our CT-Net, we also introduce a hierarchical supervision with bilinear pooling (HSBP). Experimental results demonstrate that CT-Net performs favorably against the state-of-the-art methods over three CC-reID benchmarks. Meanwhile, CT-Net also demonstrates good generalization ability by achieving comparable performance on traditional person re-ID datasets such as Market-1501 and DukeMTMC-reID.

Spiking neural networks (SNNs) have captivated the attention worldwide owing to their compelling advantages in low power consumption, high biological plausibility, and strong robustness. However, the intrinsic latency associated with SNNs during inference poses a significant challenge, impeding their further development and application. This latency is caused by the need for spiking neurons to collect electrical stimuli and generate spikes only when their membrane potential exceeds a firing threshold. Considering the firing threshold plays a crucial role in SNN performance, this article proposes a self-driven adaptive threshold plasticity (SATP) mechanism, wherein neurons autonomously adjust the firing thresholds based on their individual state information using unsupervised learning rules, of which the adjustment is triggered by their own firing events. SATP is based on the principle of maximizing the information contained in the output spike rate distribution of each neuron. This article derives the mathematical expression of SATP and provides extensive experimental results, demonstrating that SATP effectively reduces SNN inference latency, further reduces the computation density while improving computational accuracy, so that SATP facilitates SNN models to be with low latency, sparse computing, and high accuracy.

Multi-label pedestrian attribute recognition (PAR) involves assigning multiple attributes to pedestrian images captured by video surveillance cameras. Despite its importance, learning robust attribute-related features for PAR remains a challenge due to the large intra-attribute variations in the image space. These variations, which stem from changes in pedestrian poses, illumination conditions, and background noise, make extracted attribute-related features susceptible to irrelevant information or noise interference. Existing PAR methods rely on body prior extractors or attention mechanisms to locate attribute-correlation regions for extracting robust features. However, these methods may not be robust to intra-attribute variations, which limits their effectiveness. To address this challenge, we propose a novel and flexible PAR framework that leverages the exponential information bottleneck (ExpIB) approach. Our ExpIB-Net uses mutual information compression as the main penalty during the early stage of training, thereby eliminating irrelevant information. As training progresses, the mutual information penalty weakens and the Binary Cross-Entropy Loss (BCELoss) contributes to improving the PAR recognition accuracy. Our method can also be integrated into an attention module to form the AttExpIB-Net, which better handles intra-attribute variations for better performance. Additionally, our model-agnostic ExpIB approach is plug-and-play, requiring no additional computational overhead during inference. Experiments on several challenging PAR datasets show that our method outperforms state-of-the-art approaches.

The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. However, this learning rule needs extra time for updating operations at each step, causing extra energy consumption and reducing the computational efficiency. The event-driven or spike-based coding strategy of spiking neural networks (SNNs), i.e., neurons will only be active if driven by continuous spiking trains, employs all-or-none pulses (spikes) to transmit information, contributing to sparseness in neuron activations. In this article, we propose two event-driven IP learning rules, namely, input-driven and self-driven IP, based on basic IP learning. Input-driven means that IP updating occurs only when the neuron receives spiking inputs from its presynaptic neurons, whereas self-driven means that IP updating only occurs when the neuron generates a spike. A spiking convolutional neural network (SCNN) is developed based on the ANN2SNN conversion method, i.e., converting a well-trained rate-based artificial neural network to an SNN via directly mapping the connection weights. By comparing the computational performance of SCNNs with different IP rules on the recognition of MNIST, FashionMNIST, Cifar10, and SVHN datasets, we demonstrate that the two event-based IP rules can remarkably reduce IP updating operations, contributing to sparse computations and accelerating the recognition process. This work may give insights into the modeling of brain-inspired SNNs for low-power applications.

Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis. However, due to the large intra-class variation of a person across cameras, e.g., occlusions, illuminations, viewpoints, and poses, Re-ID is still a challenging task in the field of computer vision. In this paper, to attack the issues concerning with intra-class variation, we propose a coarse-to-fine Re-ID framework with the incorporation of auxiliary-domain classification (ADC) and second-order information bottleneck (2O-IB). In particular, as an auxiliary task, ADC is introduced to extract the coarse-grained essential features to distinguish a person from miscellaneous backgrounds, which leads to the effective coarse- and fine-grained feature representations for Re-ID. On the other hand, to cope with the redundancy, irrelevance, and noise contained in the Re-ID features caused by intra-class variations, we integrate 2O-IB into the network to compress and optimize the features, without increasing additional computation overhead during inference. Experimental results demonstrate that our proposed method significantly reduces the neural network output variance of intra-class person images and achieves the superior performance to state-of-the-art methods.

This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule referred to as IP is employed for adjusting the radial basis functions (RBFs), resulting in a neural network (NN) with both weights and excitability tuning, based on which neuroadaptive tracking control algorithms for multiple-input-multiple-output (MIMO) uncertain systems are derived. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.