This paper addresses the critical challenge of detecting and interpreting cybersecurity anomalies in Autonomous Vehicles (AVs) under high-frequency cyberattacks using a Particle filter. In this approach, we leverage the power of Particle filter-based state estimation, combining it with suitably defined thresholds and anomaly detection metrics to detect cyberattacks. In addition, to demonstrate the superior performance of the Particle filter for cyberattack detection, a comparison between the Kalman filter and the Particle filter has been conducted. The simulation results performed on the HuskyA200 autonomous ground vehicle (AGV) demonstrated that the Particle filter provides superior performance and interpretability during high-frequency attacks compared to the Kalman filter. The feedback from Particle filter-based detection can help the control functions of the vehicle, such as velocity damping and orientation correction, mitigate attack impacts for real-time operation.

In advanced manufacturing, strict safety guarantees are required to allow humans and robots to work together in a shared workspace. One of the challenges in this application field is the variety and unpredictability of human behavior, leading to potential dangers for human coworkers. This paper presents a novel control framework by adopting safety-critical control and uncertainty estimation for human-robot collaboration. Additionally, to select the shortest path during collaboration, a novel quadratic penalty method is presented. The innovation of the proposed approach is that the proposed controller will prevent the robot from violating any safety constraints even in cases where humans move accidentally in a collaboration task. This is implemented by the combination of a time-varying integral barrier Lyapunov function (TVIBLF) and an adaptive exponential control barrier function (AECBF) to achieve a flexible mode switch between path tracking and collision avoidance with guaranteed closed-loop system stability. The performance of our approach is demonstrated in simulation studies on a 7-DOF robot manipulator. Additionally, a comparison between the tasks involving static and dynamic targets is provided.Note to Practitioners—This research addresses the need to improve the safety of robots interacting with humans when performing collaborative tasks. Existing safety-critical control (SCC) approaches do not adequately monitor and continuously limit the state of the robot in Cartesian space, which results in a risk of injury to human operators if there is unexpected behavior during collaboration. Additionally, existing SCC approaches only consider system uncertainty for a single task (i.e. path tracking only or collision avoidance only). These problems limit the applicability of SCC techniques to manufacturing cobots. We address these problems by developing a controller that accounts for uncertainty in robot dynamics, guarantees that the robot end-effector remains within a constrained task space, and continuously modifies its motion in real-time to avoid dynamic obstacles that violate this space. We employ a machine learning approach to estimate the unknown uncertainties in real-time, allowing them to be incorporated within the controller design. The designed controller selects the shortest path for collision avoidance at each sample instant in order to minimize the total motion of the robot.

This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time. First, a formation control protocol based on a fixed-time backstepping sliding mode control is designed, in which the consensus cooperative tracking errors for each AUV will be formulated. Then, to compensate for the saturated control torques, an adaptive auxiliary variable is introduced. Finally, a fixed-time adaptive fuzzy logic control (FLC) is derived to approximate the unknown dynamics, in which the adaptive laws of the FLC is derived such that the adaptive signals and errors can be convergent within a fixed time. The fixed-time convergence is desired in practice because it provides an exciting property that the global convergence of the whole system is independent with the initial states of the AUVs. The computer simulation results for a consensus formation control of four AUVs show that the proposed formation control can provide higher tracking performance with lower and smoother control efforts.

In this paper we present the implementation of a Control Barrier Function (CBF) safety filter that provides obstacle avoidance for a robotic manipulator arm system in a simulated environment (Simulink). CBF is a control technique that has developed over the past decade and has been extensively explored in the literature on its mathematical foundations and potential applications for a variety of safety-critical control systems. In this work we will look at the design of CBF for the robotic manipulator obstacle avoidance, discuss the selection of the CBF parameters and present a search algorithm to find parameters that provide the most efficient trajectory for different obstacles. We then create a data-set across a range of obstacle scenarios that is used to train a Neural-Network (NN) model that can be used within the control scheme to allow the system to efficiently adapt to different obstacle scenarios.

This article aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: 1) the faster response, lower tracking errors, lower chattering, and higher robustness of the FTC, 2) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and 3) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analyzed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

3D skeleton-based motion prediction and activity recognition are two interwoven tasks in human behaviour analysis. In this work, we propose a motion context modeling methodology that provides a new way to combine the advantages of both graph convolutional neural networks and recurrent neural networks for joint human motion prediction and activity recognition. Our approach is based on using an LSTM encoder-decoder and a non-local feature extraction attention mechanism to model the spatial correlation of human skeleton data and temporal correlation among motion frames. The proposed network can easily include two output branches, one for Activity Recognition and one for Future Motion Prediction, which can be jointly trained for enhanced performance. Experimental results on Human 3.6M, CMU Mocap and NTU RGB-D datasets show that our proposed approach provides the best prediction capability among baseline LSTM-based methods, while achieving comparable performance to other state-of-the-art methods.

This article develops a new strategy for robust fault-tolerant control (FTC) of robot manipulators using an adaptive fuzzy integral sliding-mode control (ISMC) and a disturbance observer (DO). First, an ISMC is developed for the FTC system. The major features of the approach are discussed. Then, to enhance the performance of the system, a fuzzy logic system approximation and a DO are introduced to approximate the unknown nonlinear terms, which include the model uncertainty and fault components, and to estimate the compounded disturbance and then are integrated into the ISMC. Next, a switching term based on an adaptive two-layer supertwisting algorithm is designed to compensate the disturbance estimated error and guarantee stability and convergence of the whole system. The nominal controller of the ISMC is reconstructed using a backstepping control technique to achieve the stability for the nominal system based on the Lyapunov criterion. The computer simulation results demonstrate the effectiveness of the proposed approach.

This paper develops a novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC). In this approach, a novel backstepping nonsingular fast terminal sliding mode controller (BNFTSMC) is developed based on an integration of integral nonsingular fast terminal sliding mode surface and a backstepping control strategy. The benefits of this approach are that the proposed controller can preserve the merits of the integral nonsingular fast terminal sliding mode control (NFTSMC) in terms of high robustness, fast transient response, and finite-time convergence, as well as backstepping control strategy in terms of globally asymptotic stability based on Lyapunov criterion. However, the major limitation of the proposed BNFTSMC is that its design procedure is dependent on the prior knowledge of the bound value of the disturbance and uncertainties. In order to overcome this limitation, an adaptive technique is employed to approximate the upper bound value; yielding an ABNFTSMC is recommended. The proposed controller is then applied for tracking control of a PUMA560 robot and compared with other state-of-the-art controllers, such as computed torque controller, PID controller, conventional PID-based sliding mode controller, and NFTSMC. The comparison results demonstrate the superior performance of the proposed approach.

This study develops a fault estimation and accommodation scheme for the image-based visual servoing system to eliminate the effects of the faults due to the image feature extraction task, which is named as bias virtual sensor fault. First, a bias virtual sensor fault in visual servoing is declared. Then, fault diagnosis, which includes fault detection, isolation, and estimation, is designed based on the means of particle filter. Finally, a fault accommodation law is developed based on the information obtained from the fault estimation to compensate for the effects of the fault in the system. The proposed fault estimation and accommodation is verified through simulation and experimental studies, and the results show that the system can estimate and eliminate the unknown fault effects effectively.

In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis scheme. In this scheme, an online fault estimation algorithm based on time delay estimation (TDE) is proposed to approximate actuator faults. The estimated fault information is used to detect, isolate, and accommodate the effect of the faults in the system. Then, a robust AFTC law is established by combining the obtained fault information and a robust NFTSMC. Finally, a high-order sliding mode (HOSM) control based on super-twisting algorithm is employed to eliminate the chattering. In comparison to the PFTC and other state-of-the-art approaches, the proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation. Finally, simulation results are obtained to verify the effectiveness of the proposed strategy.

Due to the robustness against the uncertainties, conventional sliding mode control (SMC) has been extensively developed for fault-tolerant control (FTC) system. However, the FTCs based on conventional SMC provide several disadvantages such as large transient state error, less robustness, and large chattering, that limit its application for real application. In order to enhance the performance, a novel adaptive third-order SMC, which combines a novel third-order sliding mode surface, a continuous strategy and an adaptation law, is proposed. Compared with other innovation approaches, the proposed controller has an excellent capability to tackle several types of actuator faults with an enhancing on robustness, precision, chattering reduction, and time of convergence. The proposed method is then applied for an attitude control of a spacecraft and the results demonstrate the superior performance.

In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.

In this paper, the fault diagnosis (FD) problem in image-based visual servoing with eye-in-hand configurations is investigated. The potential failures are detected and isolated based on approximating parameters related. First, the failure scenarios of the visual servoing systems are reviewed and classified into the actuator and sensor faults. Second, a residual generator is proposed to detect the failure occurrences, based on the Kalman filter. Third, a decision table is proposed to isolate the fault type. Finally, simulation and experimental results are given to validate the efficacy and the efficiency of the proposed FD strategies.