This paper presents a fixed-time equivalent-input-disturbance (EID) approach to deal with the problem of robust output-feedback control for perturbed uncertain systems. This method uses the basic structure of the conventional EID approach and treats uncertainties and disturbances as a lumped disturbance on the control-input channel. A fixed-time state observer enables state estimation, which resolves the causality issue in an EID-based control system, is finished in a fixed time. An implicit Lyapunov function, the homogeneity with dilation, the input-to-state stability, and the small-gain theorem are used to analyze the convergence and robustness of the EID-based system with measurement noise. Numerical and experimental results are presented to demonstrate the effectiveness and superiority of the proposed method.

This paper deals with contour tracking control of a networked multi-axis motion control system (NMCS) with repetitive tasks. To improve the control performance of the system under the overall effects of aperiodic timevarying delays and exogenous disturbances, a networkinduced delay is first modeled to be a disturbance. Next, an improved equivalent-input-disturbance (IEID) approach is devised to suppress the overall effect. Then, an iterative learning controller is designed in combination with the IEID estimator to perform individual-axis tracking control. The stability of the system is analyzed using the separation theorem. After that, a cross-coupled controller is used to further improve contour-tracking performance. Finally, simulations and experiments are carried out to verify the feasibility and effectiveness of the new approach.

This article is concerned with developing a privacy-preserving distributed-learning-based fault diagnosis approach for power transformers. Due to the constraints of data privacy, it is not possible to have enough labeled samples for training. Recently, the emergence of federated learning (FL) has provided a secure and distributed learning framework. However, the unbalanced data from multiple power stations may reduce the overall performance of FL while an untrusted central server can threaten the data privacy and security of clients. To address such challenges, a privacy-preserving FL scheme is developed for transformer fault diagnosis, where a multistep data-sharing strategy and an adaptive differential privacy technology are proposed. Specifically, amounts of shared data and noise perturbation will be designed according to the quantity of local data by the central server. The experimental results on the dataset generated according to IEC publication 60599 show that the proposed method has high diagnostic accuracy across various categories of transformer faults and even on training datasets with extremely unbalanced data quantity where the average accuracy is as high as 95.28%.

Low-cost inertial measurement units (IMUs) suffer from low sensitivity and high random walk noise, which makes it challenging to use them directly for dead reckoning. Regular model-based inertial navigation methods require accurate modeling of IMU noise to get better results, while learning-based methods need plentiful datasets. In this article, a novel low-cost IMU dead-reckoning algorithm for wheeled mobile robot is introduced by integrating model-based and learning-based approaches, which inherits the merits of both methods. It achieves the dead reckoning by using invariant extended Kalman filter (InEKF) and IMU error model, and computes the noise parameters of the model with the aid of a deep-learning-based method. Our deep-learning-based strategy is designed to obtain noise-reduced inertial information of robots from low-cost IMU data such that the InEKF can converge. The experimental results show that the proposed method can accurately estimate the attitude, velocity, and position of the wheeled mobile robot, and can compete with vision algorithms. In addition, our proposed method consumes few computational resources to satisfy the needs of low-cost applications.

This article addresses the disturbance rejection problem for networked control systems subject to time-varying delays and measurement noise. First, the uncertainty caused by the delay is modeled to be a delay-induced disturbance and treated as part of the lumped disturbance. Second, an adaptive-filter-based EID estimator is proposed to estimate this lumped disturbance under the effect of measurement noise. A new filter is designed by introducing an adjustable zero. Then, an adaptive characteristics-adjustment mechanism is constructed for the filter to resolve the contradiction between fast disturbance reconstruction and measurement noise attenuation. Finally, experimental results reveal the superiority of the proposed method.

In object goal navigation tasks, the robot’s understanding of semantic relationships in the environment is a key factor in its ability to localize target objects. Previously, learning-based methods trained robots using 3D scene datasets to learn semantic relationships. However, these approaches perform poorly in new environments with unfamiliar semantic contexts. In this paper, we propose ChatNat which leverages the powerful knowledge summarizing and reasoning capabilities of a Large Language Model (LLM) for zero-shot inference of explicit semantic relationships. These relationships are further integrated into the navigation system for efficient localization of target objects. ChatNav employs a spatial object clustering algorithm to collect semantic clues and designs common-sense-based prompts for interacting with LLM. It then uses a gravity-repulsion model to convert inference results into heuristic factors for robust navigation decision-making. Our approach requires no additional training and can consistently obtain accurate semantic relationships from LLM, making it well-suited for navigating unknown environments. Experimental results demonstrate the outstanding navigation performance of our proposed method on the Gibson and HM3D datasets, surpassing the current state-of-the-art object goal navigation methods.