This article investigates the fixed-time distributed average tracking (DAT) problem for nonlinear multiagent systems with unity relative degree under external disturbances. A distributed control framework is developed to guarantee fixed-time convergence of all agents' outputs to the target trajectory, which is defined as the average of multiple nonlinear reference signals. The approach consists of three main components. First, a steady-state generator is introduced to reconstruct the desired trajectory. Using this generator, a distributed observer is designed to estimate the target trajectory while ensuring robustness against initialization errors. Subsequently, an observer-based output-feedback controller is developed to guarantee the convergence of each agent's output to its corresponding reference signal within a fixed time. Through rigorous theoretical analysis, it is proved that the proposed control architecture ensures fixed-time convergence to the target trajectory, effectively solving the fixed-time DAT problem. The effectiveness of the proposed method is validated through numerical simulations.

A popular path learning method is to use off-line programming by demonstration (PbD) to plan a rough path, but it is subjected to uncertainties in the environment so needs to be updated during the task execution. For this purpose, a spatial iterative learning control (sILC) is developed to learn an accurate path through intuitive online correction based on human-robot interaction (HRI). To improve the efficiency and accuracy of the path learning, a visual assistance system is added to HRI, which helps the robot to find the initial path point and complement the correction of the learning error. This method mitigates the requirement on classic ILC that the time period should be consistent in the repetitive interaction task and utilizes the complementary advantages of vision and force sensing, thus addressing the limitations of the vision-based or HRI methods. The rigorous proof of learning convergence and the results of the simulation and experiments on a 7-degree-of-freedom (DoF) Sawyer robot platform illustrate the significance and advantages of the proposed method.Note to Practitioners—The problem of accurate path learning of robotic manipulators is addressed in this paper, which is found in ample applications such as welding and laser cutting. When the required path is irregular, it is difficult to define it based on offline programming and calibration. This paper presents a new human-robot interactive learning framework, in which the interaction force and machine vision are combined with sILC to achieve online detection and correction for learning and tracking an unknown path. This framework leads to an intuitive human-robot collaboration system where the human operator can fine tune the robot’s motion through direct physical interaction, and at the same time the robot improves its tracking performance automatically based on visual servoing.

This paper presents a novel method for learning and tracking of the desired path of the human partner in physical human-robot interaction. Combining the Adam optimization algorithm with iteration learning control (ILC), a path learning method is designed to generate and update reference waypoints according to the human partner’s desired path. This method firstly uses the Adam optimization algorithm to update the robot’s reference waypoints in an online manner. Then, an ILC is developed to further modify the waypoints and reduce the difference between the robot’s actual path and the human partner’s desired path in an iterative manner. Simulations and experiments on a 7-DOF Sawyer robot are carried out to show the effectiveness of our proposed method.

As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying the performance degradation of bogie is critical in determining a particular maintenance strategy. In this article, by considering the intrinsic link between fault type and performance degradation of bogie, a novel multiple convolutional recurrent neural network (M-CRNN) that consists of two CRNN frameworks is proposed for simultaneous diagnosis of fault type and performance degradation state. Specifically, the CRNN framework 1 is designed to detect the fault types of the bogie. Meanwhile, CRNN framework 2, which is formed by CRNN Framework 1 and an RNN module, is adopted to further extract the features of fault performance degradation. It is worth highlighting that M-CRNN extends the structure of traditional neural networks and makes full use of the temporal correlation of performance degradation and model fault types. The effectiveness of the proposed M-CRNN algorithm is tested via the HST model CRH380A at different running speeds, including 160, 200, and 220 km/h. The overall accuracy of M-CRNN, i.e., the product of the accuracies for identifying the fault types and evaluating the fault performance degradation, is beyond 94.6% in all cases. This clearly demonstrates the potential applicability of the proposed method for multiple fault diagnosis tasks of HST bogie system.

This paper studies bipartite tracking problem of nonlinear multiagent systems over signed directed graphs. Each following agent is modeled by a higher-order nonlinear system in strict-feedback form with unknown dynamics and hysteresis input uncertainty. Both distributed state feedback and output feedback control laws are proposed to achieve bipartite tracking confined by the prescribed performance bounds. The proposed approximation-free distributed controllers only utilize error variables incorporating with performance bound functions, which lead to a low-complexity control algorithm. Moreover, the proposed control laws guarantee that all signals of the closed-loop system are uniformly ultimately bounded.

Fault diagnosis of high-speed train (HST) bogie is essential in guaranteeing the normal daily operation of an HST. In prior works, feature extraction from multisensor vibration signals mainly relies on signal processing methods, which is independent of the classification process. Based on convolutional neural networks (CNNs), this paper presents a novel fault diagnosis system using the residual-squeeze net (RSNet), which is directly applicable to raw data (time sequences) and does not require any signal transformation or postprocessing. In this network, information fusion is achieved by using the convolutional layer. More specifically, via the squeeze operation, an optimal combination of channels is learnt by training the network. Experimental results obtained by using SIMPACK simulation data demonstrate the effectiveness of the proposed approach in both complete failure case and single failure case, with diagnosis accuracy near 100%. The proposed approach also shows good performance in identifying the locations of faulty components. Comparisons between RSNet and competitive methods shows the advantages of RSNet for fault classification.

This paper presents a novel control scheme for trajectory tracking of valve-controlled electro-hydraulic actuators in lower limb load exoskeleton. First, the mathematical model of the electro-hydraulic system is developed in a strictly feedback form, where constant unknown mass and periodic unknown equivalent force acted on the electro-hydraulic actuator are involved. Then, under the framework of backstepping design, a controller consisting of an adaptive robust control and a repetitive learning control is proposed to learn the unknowns, respectively. The tracking convergence of the closed-loop system is proved rigorously in a Lyapunov way. Finally, the control performance of the proposed controller is verified via simuatlion.

Railway systems may be interrupted by unforeseen events that require quick replanning to a feasible new schedule. This paper deals with the train rescheduling problem on double-track lines. The rescheduling problem is regarded as a conflict detection and resolution procedure. Timed Colored Petri nets are adopted to model the railway system: places represent rail resources, and tokens represent trains. A conflict detection rule is established in accordance with the safety principles of railway operations to predict potential conflicts. A Petri-net-based conflict resolution algorithm adapted from the A* algorithm is designed to search for an optimal or a near-optimal feasible schedule. The algorithm takes into account the railway operational principles when generating new markings, so that the new schedule has less train delays and respects the safety principles. The approach is applied in a case study to a double-track corridor from the Dutch railway network. For small delays, the algorithm can make delayed trains recover to their scheduled timetable within seconds. For large perturbations, the solutions generated by the algorithm can effectively reduce train delays while ensuring traffic safety.

This paper is concerned with modeling and prediction of lung tumor motion for a new adaptive tumor tracking system in radiotherapy. Information of the tumor motion is used to control a robotic treatment couch, which moves in six degrees of freedom and maintains the tumor in a certain fixed spatial position. With this new technology it is possible to avoid gating during the radiotherapy as well as active breathing control, such that duration and cost of the therapy as well as trauma of the patient can be reduced. The tumor position is detected by an electronic portal imaging device (EPID) such that internal markers for the tumor tracking are no longer necessary. An infrared (IR) camera is introduced to detect abdominal motion, which provides a reference to the respiratory signal and can be used to predict the tumor position over a complete breathing period. This paper presents the effort of tumor motion modeling and prediction. Main challenges with the proposed method in this context are: lack of breathing control causes irregular respiration, low data rate of the EPID device delivers too few information about the tumor position. Two methods, namely an adaptive filtering and a nonlinear method based on Takens Theorem, have been investigated and compared with respect to diverse criteria including prediction accuracy, model complexity and computational effort. Performance analysis with clinical data has shown that both methods can well describe correlation between the EPID and the IR signals, while the adaptive filter possesses advantage with respect of computational effort.