Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of-distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject-independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.

This article proposes a long-term dynamic object relocalization (L-DOR) solution to address the challenge of mobile robots efficiently relocalize task-related objects in human–robot coexisting environments over extended periods. Existing methods mainly focus on one-time object localization, neglecting long-term relocalization amidst dynamic changes caused by human activities. To tackle this issue, a probabilistic model of object distribution based on spatio-temporal patterns is first established. Then, a robot-object perception interaction is introduced to achieve dynamic object category discrimination, enabling the robot to handle sporadic events caused by human activities. Besides, a cost-expectation balance-based object matching method is presented to determine the suitable task object and gradually infer its potential locations with probabilistic model. On this basis, we suggest a hierarchical global planning and local decision-making to prioritize search efforts to improve localization efficiency. Extensive comparisons and long-term experiments in real-world scenarios with a Fetch robot demonstrate the efficacy of L-DOR in terms of search performance, adaptability to different scenarios, and long-term effectiveness.

The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial features, neglecting valuable local information, potentially leading to suboptimal performance. Therefore, this study proposed a dynamic hierarchical convolutional attention network (DH-CAN) that comprehensively learned discriminative information from both global and local spatial domains, as well as from time-frequency domains in EEG signals. Specifically, a multiscale convolutional block was designed to dynamically capture time-frequency information. The channels of EEG signals were mapped to different brain regions based on motor imagery neural activity patterns. The spatial features, both global and local, were then hierarchically extracted to fully exploit the discriminative information. Furthermore, regional connectivity was established using a graph attention network, incorporating it into the local spatial features. Particularly, this study shared network parameters between symmetrical brain regions to better capture asymmetrical motor imagery patterns. Finally, the learned multilevel features were integrated through a high-level fusion layer. Extensive experimental results on two datasets demonstrated that the proposed model performed excellently across multiple evaluation metrics, exceeding existing benchmark methods. These findings suggested that the proposed model offered a novel perspective for EEG decoding research.

One of the challenges for articulated heavy vehi-cles (AHVs) is the complexity of vehicle operating conditions, particularly on low-adhesion road and at high speed, which may result in AHVs' jackknifing. This study proposes a novel method to address the jackknifing issue by differential braking. Specifically, a fusion controller is used Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) to suppress the jackknife. Firstly, a controller-oriented 3 DoF model is established, and its parameters are optimized by an algorithm named Genetic. Then, the proposed controller utilizes differential braking to get the additional yaw moment, enhancing the lateral agility and responsiveness of AHVs without compromising their stability. Afterwards, the simulation test is conducted. Finally, the results demonstrate that the controller helps AHVs achieve anti-jackknifing.

Autonomous vehicles (AVs) bring advantages such as comfort, safety, and eco-friendliness compared to conventional ones. The focus on comfort has become increasingly important as it plays a key role in the widespread acceptance of AVs. Passenger demand for alleviating motion sickness (MS) during travel has also driven extensive research in this area. This manuscript conducts a comprehensive and comparative review of published articles to provide up-to-date research outcomes on MS mitigation. It also examines the optimization methods employed over the past two decades to enhance motion comfort in AVs. The methods for detecting and resolving MS are elaborated upon, and the research gap and challenges are addressed. Furthermore, the manuscript presents the current efficacy of the research and proposes an anti-motion sickness control framework that utilizes a cloud control platform, which might offer potential future research directions in the field of autonomous driving. This study serves as a valuable reference for future efforts and opportunities to improve the motion comfort of AVs.

This paper focuses on adaptive robust vibration control for flexible riser systems affected by input nonlinearities and unknown external disturbances. An auxiliary system is constructed and adjusted to develop a boundary adaptive robust control for restraining the vibrational offset and eliminating the effect of input nonlinearities. Besides, an adaptive law of boundary disturbance upper-bound is constructed together with the vibration control strategy to estimate the magnitude of unknown boundary disturbance. Further, the convergence of states and stability of the system are ensured with the developed control scheme. By choosing the proper control parameters, the control performance is verified with the obtained simulation results.

This paper presents a framework of output feedback stabilization of an axially moving accelerated system influenced by exogenous disturbances. Based on the flexible-rigid coupled dynamical model of the system, a boundary output feedback control is developed to reduce the vibration and estimate the boundary disturbance by fusing Lyapunov's synthetic approach, disturbance rejection theory, and observer technique. Under the developed control, the exponential convergence of system state observer errors and uniformly ultimately bounded stability of the controlled system can be demonstrated adopting rigorous theoretical analysis, and the control performance of the developed control method is verified by simulation results.

This paper focuses on the fuzzy-H∞-filter-design problem for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Two cases of the time-varying delays are considered: 1) The delays are differentiable and have both the lower and upper bounds of the delay derivatives, and 2) the delays are bounded but not necessary to be differentiable. Since we employ a new fuzzy Lyapunov-Krasovskii functional (LKF) and estimate a tighter upper bound of its derivative, the proposed delay-derivative-dependent bound-real-lemma (BRL) condition has advantages over some previous results in that it enlarges the application scope but has less conservatism, which is established theoretically. The BRL condition that depends on both the upper and lower bounds of the delay derivatives is derived. Then, based on the aforesaid BRL, a new fuzzy H∞filter scheme is proposed, and a sufficient condition for the existence of such a filter is established in terms of linear-matrix inequalities (LMIs). Finally, some numerical examples and an application to the truck-trailer system are utilized to demonstrate the effectiveness and reduced conservatism of our results.