This article studies the fault-tolerant consensus problem with the guaranteed transient performance of multiagent systems (MASs) subject to unknown time-varying actuator faults and disturbances. The general actuator faults, including both multiplicative and additive time-varying faults, are considered in such a problem for the first time. Both single-integrator modeled agents and double-integrator modeled agents are investigated. The transient performance is ensured in the sense that position errors between each pair of neighboring agents are guaranteed within certain user-defined time-varying performance bounds. Adaptive laws are designed to estimate information about faults and disturbances. For MASs with additive faults, the proposed controllers ensure errors asymptotically converge to zero with guaranteed transient performance. For MASs with both multiplicative faults and additive faults, the proposed controllers ensure errors converge to a residual set without asymptotic convergence but still with guaranteed transient performance. Two simulation examples are provided to evaluate the proposed schemes.

The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model’s robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN.

Image processing has significantly extended the practical value of the eye-in-hand camera, enabling and promoting its applications for quantitative measurement. However, fully vision-based pose estimation methods sometimes encounter difficulties in handling cases with deficient features. In this article, we fuse visual information with the sparse strain data collected from a single-core fiber inscribed with fiber Bragg gratings (FBGs) to facilitate continuum robot pose estimation. An improved extreme learning machine algorithm with selective training data updates is implemented to establish and refine the FBG-empowered (F-emp) pose estimatoronline. The integration of F-emp pose estimation can improve sensing robustness by reducing the number of times that visual tracking is lost given moving visual obstacles and varying lighting. In particular, this integration solves pose estimation failures under full occlusion of the tracked features or complete darkness. Utilizing the fused pose feedback, a hybrid controller incorporating kinematics and data-driven algorithms is proposed to accomplish fast convergence with high accuracy. The online-learning error compensator can improve the target tracking performance with a 52.3%–90.1% error reduction compared with constant-curvature model-based control, without requiring fine model-parameter tuning and prior data acquisition.

Cardiac electrophysiology is an effective treatment for atrial fibrillation, in which a long, steerable catheter is inserted into the heart chamber to conduct radio frequency ablation. Magnetic resonance imaging (MRI) can provide enhanced intraoperative monitoring of the ablation progress as well as the localization of catheter position. However, accurate and real-time tracking of the catheter shape and its efficient manipulation under MRI remains challenging. In this article, we designed a shape tracking system that integrates a multicore fiber Bragg grating (FBG) fiber and tracking coils with a standard cardiac catheter. Both the shape and positional tracking of the bendable section could be achieved. A learning-based modeling method is developed for cardiac catheters, which uses FBG-reconstructed three-dimensional curvatures for model initialization. The proposed modeling method was implemented on an MRI-guided robotic platform to achieve feedback control of a cardiac catheter. The shape tracking performance was experimentally verified, demonstrating 2.33° average error for each sensing segment and 1.53 mm positional accuracy at the catheter tip. The feedback control performance was tested by autonomous targeting and path following (average deviation of 0.62 mm) tasks. The overall performance of the integrated robotic system was validated by a pulmonary vein isolation simulator withex-vivotissue ablation, which employed a left atrial phantom with pulsatile liquid flow. Catheter tracking and feedback control tests were conducted in an MRI scanner, demonstrating the capability of the proposed system under MRI.

This article is focused on the design of an output tracking control scheme for a class of continuous-time periodic piecewise time-varying systems (PPTVSs) with actuator saturation and nonlinear perturbations. The energy-to-peak tracking performance is studied based on an equivalent condition on the definiteness property of matrix polynomials. Considering the actuator saturation and nonlinear perturbation, matrix polynomial-based sufficient conditions are derived through the Lyapunov method using periodic matrix functions. From a perspective of subinterval segmentation aimed at PPTVSs, the proposed conditions can achieve less conservatism for tracking the output of a periodic time-varying reference system, while the controller gains can be computed using convex optimization. Moreover, a heuristic algorithm is constructed to simultaneously guarantee the closed-loop state convergence and the output tracking performance. The reduction in conservatism and the effectiveness of algorithm are demonstrated by illustrative case studies.

This article considers the problem of designing lead, lag, and lead-lag compensators for multi-input-multi-output (MIMO) linear systems under the H∞performance measure. This is the first time that the lead, lag and lead-lag compensators are generalized to the MIMO cases by preserving their classical compensator structures. Theoretical results on the stability analysis and synthesis of MIMO systems under the H∞control performance with the proposed lead, lag, and lead-lag compensators are obtained. Then relevant algorithms for designing lead, lag, and lead-lag compensators are provided to determine the compensator parameters. Differing from traditional design methods, which mostly rely on some trial-and-error procedures, the proposed methods are algorithmic and the compensators can be synthesized systematically. Illustrative examples are used to demonstrate the effectiveness and advantages of the proposed methods.

In this paper, a novel monitoring approach is developed for nonlinear processes based on available measurements. To cope with complicated process nonlinearity, we implement total projection to latent structures (T-PLS) in each local model created by the locally weighted projection regression (LWPR) algorithm. Under the framework of locally weighted learning, four improved test statistics are established to detect potential process faults. The test statistics are not only capable to monitor the abnormal changes in the relevant subspaces, but also with thresholds suitable for non-Gaussian measurements. The effectiveness of our proposed approach is further demonstrated by a numerical nonlinear case.

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.