Auditory brainstem response (ABR) is an important tool for newborn hearing screening and neurological assessment. However, its signals are often difficult to be accurately resolved due to noise interference and weak waveforms, and the need for repeated measurements under multiple sound intensity conditions results in time-consuming data acquisition. Therefore, there is an urgent need to develop an automatic classification model with high accuracy, robustness and good interpretability to achieve stable and effective recognition performance with minimal ABR data. This study presents FIGNet, a new deep learning model that combines type-2 fuzzy logic with a time-irreversible attention mechanism to address uncertainty and temporal direction in ABR signals. Fuzzy attention helps reduce the impact of noise, while the irreversible attention models the one-way nature of neural responses. Experiments on real ABR datasets show that FIGNet outperforms existing models in both binary and five-class classification tasks. It achieves 93.72% accuracy in binary classification and 84.42% accuracy in five-class classification. Visualization results-including confusion matrices, and accuracy curves under different noise levels-further confirm that FIGNet can focus on key waveform areas and stay reliable even in noisy conditions. These findings demonstrate that FIGNet offers fast, interpretable, and robust performance for clinical ABR analysis, achieving high classification accuracy under both clean and noisy conditions.

It has been proven that electroencephalography (EEG) is an effective method for evaluating an individual's mental state. However, when it comes to the evaluation of miners' mental state, there are still some issues with missing EEG dataset and unsatisfactory evaluation accuracy. Therefore, this article proposes a miner mental state evaluation scheme with decision-level fusion based on multidomain EEG information. First, in the comprehensive lab for coal-related programs of Xi'an University of Science and Technology, the coal mine environment is simulated, and a realistic EEG dataset is constructed. Second, the multidomain features are extracted to represent abundant information in time, frequency, time-frequency, and space domain. These features with low dimension are classified adopting support vector machine (SVM), k-nearest neighbor (kNN), and back propagation (BP) network to obtain the optimal evaluation submodel (four domains corresponding to four submodels). Finally, based on the state probabilities provided by the optimal evaluation submodel, we adopt stack fusion and an improved Yager rule to fuse four submodels in order to find the most suitable fusion algorithm. The experimental results demonstrate that the average accuracy can reach 93.19% on the self-built dataset when utilizing the improved Yager rule with weight, and it realizes a better evaluation accuracy.

One of the major sources of conservativeness in min-max model predictive control (MPC) is the handling of constraints, where the ellipsoidal robust invariant set is utilized for the proposal of sufficient and conservative conditions for the satisfaction of the constraints. In this article, in order to reduce the conservativeness due to the constraint handling, we add additional relaxation variables to the physical constraints and propose a two-step approach through relaxing the constraints by the amount which is determined by the calculating of the maximal admissible set (MAS). The constraints are relaxed in an iterative manner to avoid the constraints violation, and the constraint relaxation variables are degrees of freedom for relaxing the constraints and improving the control performance. Moreover, we show that under certain circumstance, the physical constraints can be removed without the constraint violation. The proposed approach is shown to be recursively feasibility and its effectiveness is verified through an air conditioning control in a building energy system. Note to Practitioners—Buildings are account for large percentage of worldwide energy consumptions. One of the most applicable method for optimization of building energy system subject to multiple constraints is the model predictive control (MPC). However, the industrial MPC is usually not recursively feasible, which implies that the optimization problem can become infeasible and the software will be terminated at some time. In order to apply the MPC synthesis approach (MPC with recursive feasibility guarantee), we have to overcome the conservativeness problem due to the handling of constraints. We propose a useful approach in this work by introducing relaxation variables which act as degrees of freedom for improving the control performance, while the physical constraints are still satisfied. The proposed approach is verified through an example of a 24m2 office room located in Cyber-Physical Energy System (CPES) lab in Western China Science and Technology Innovation Harbour in Xianyang, China. The numerical results show the performance improve of the proposed approach.

Currently, scholars typically investigate the dynamics of two coupled partial differential systems. However, in practical engineering applications, there are often$N$coupled partial differential systems involved. In this paper, a novel$N$coupled hyperbolic-parabolic partial differential systems (HPPDS) is introduced. Firstly, the stabilization problem of the isolated HPPDS is addressed using Lyapunov functions and the backstepping method. Secondly, the synchronization problem of$N$coupled HPPDS is considered in the${L^{2}} \times {H^{1}}$sense. The synchronization problem of the target system is transformed into the stability problem of the decoupled system. The definition of synchronization error is provided, and the synchronization error system is derived. Subsequently, a boundary controller is designed using the backstepping method to achieve stability of the error system, i.e., synchronization of the$N$coupled HPPDS. Finally, two simulation examples are presented to demonstrate the validity of the obtained results.

In real-world industrial control systems, the extended usage of diverse equipment and instruments over extended periods increases the likelihood of malfunctions in any process unit. Such malfunctions can significantly impact the entire system, leading to substantial economic losses. To address these challenges, this paper proposes an actuator-sensor fault estimation and self-healing control scheme aimed at ensuring the stable and efficient operation of a discrete T-S fuzzy system. Firstly, a dual observer fault estimation method is introduced to overcome the limitations of highly conservative stability conditions and limited applicability encountered when estimating actuator and sensor faults with a single observer. Secondly, selfhealing controllers based on integral sliding mode and state feedback are individually designed. Lastly, leveraging the TS fuzzy model of the wastewater treatment plant, simulation experiments are conducted to validate the efficacy of the proposed methods. Comparative analysis of simulation results reveals that the dual observer fault estimation methods exhibit faster response speed for both faults estimation. Furthermore, in comparison to other self-healing controllers, the fuzzy-weighted self-healing controller exhibits superior overall performance

This article considers the dynamic output feedback model predictive control (DOFMPC) for the constrained Takagi-Sugeno (T-S) model with bounded disturbance. Unlike the existing approach where the robust positively invariant set is characterized by a single ellipsoid, we characterize it by the intersection of multiple ellipsoids, each corresponds to a vertex sub-model of the T-S model realization. The previous single ellipsoid is then an inner approximation of the intersection of multiple ellipsoids in this article. Therefore, the performance can be improved. We also generalize the multi-ellipsoid approach to the previous multi-step approach and formulate the so-called multi-step multi-ellipsoid approach in this article, which can further enlarge the feasibility region and enhance the performance. The recursive feasibility and the convergence of the approach are guaranteed. The proposed approaches are compared through a numerical problem to show their effectiveness.

Reconstructing visual stimulus representation is a significant task in neural decoding. Until now, most studies have considered functional magnetic resonance imaging (fMRI) as the signal source. However, fMRI-based image reconstruction methods are challenging to apply widely due to the complexity and high cost of acquisition equipment. Taking into account the advantages of the low cost and easy portability of electroencephalogram (EEG) acquisition equipment, we propose a novel image reconstruction method based on EEG signals in this article. First, to meet the high recognizability of visual stimulus images in a fast-switching manner, we construct a visual stimuli image dataset and obtain the corresponding EEG dataset through EEG signals collection experiment. Second, we introduce the deep visual representation model (DVRM), comprising a primary encoder and a subordinate decoder, to reconstruct visual stimuli representation. The encoder is designed based on residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images. Meanwhile, the decoder is designed using a deep neural network to reconstruct the visual stimulus representation from the learned deep visual representation. The DVRM can accommodate the deep and multiview visual features of the human natural state, resulting in more precise reconstructed images. Finally, we evaluate the DVRM based on the quality of the generated images using our EEG dataset. The results demonstrate that the DVRM exhibits an excellent performance in learning deep visual representation from EEG signals, generating reconstructed representation of images that are realistic and highly resemble the original images.

In this article, a novel approach to hierarchical implementation of output feedback robust model predictive control is proposed for the linear polytopic uncertain model. One optimization problem for minimizing the performance index is followed with the other assessing estimation error set (EES). The two problems are posed in a lexicographic order. Since in the latter problem, the controller parametric matrices are retaken as the degrees of freedom for the optimization, a much less conservative EES is calculated. Therefore, by applying the new approach, the control performance can be greatly improved as compared with the earlier schemes without lexicographic optimization. The proposed approach is proven to be recursively feasible, and the closed-loop stability is specified by the notion of quadratic boundedness. The result is verified through two numerical examples.

In this article, we present a codesign of measurement quantized dynamic output feedback model predictive control (DOFMPC) for the Takagi–Sugeno model with bounded disturbance. The system output is quantized by a dynamic quantizer before it is transmitted to the DOFMPC controller. Hence, we utilize the dynamic output feedback control law with a quantized output signal and consider the mixed input and quantized output constraint for the controller design. By optimizing the quantizer and controller parameters online, the control performance is enhanced. Moreover, we formulate a two-leveled optimizations, with the upper level optimizing the performance index and the lower level optimizing the soft constraint in a lexicographic order, for the codesign of the DOFMPC controller and dynamic quantizer. Thus, there are more degrees of freedom for tightening the soft constraints. The recursive feasibility and stability of the proposed approaches are guaranteed. The applicability of the proposed approach is illustrated by a simulation example.

In recent years, brain–machine interface (BMI) technology has made great progress in controlling external devices and restoring motor function for people with disabilities. To better optimize BMI system performance, in this article, we summarize and describe a universal closed-loop BMI system framework and review the latest developments over the past ten years from a control perspective. First, the basic BMI systems with open-loop and closed-loop structures are introduced in chronological order. Second, the units of the universal closed-loop BMI system, i.e., the decoder, encoder, and auxiliary controller, are reviewed and summarized in terms of principles, categories, and algorithms. Finally, from research and practical perspectives, the importance of biomimetic brain models, great challenges, and future developments are discussed based on current progress. With this analysis of the universal closed-loop framework, this review can provide necessary theoretical guidance for the research and development of BMI systems.

This paper considers predictive control of a Takagi-Sugeno fuzzy model with bounded disturbance, strictly satisfying the input and state constraints. The membership-function-dependent dynamic output feedback law, as in a previous work, is utilized. The novelty lies in the following technical improvements. For the notion of quadratic boundedness, which specifies closed-loop stability and invariance properties, the full Lyapunov matrix is utilized. The real-time ellipsoidal bound of true state is recursively optimized by invoking the S-procedure. Some relaxation scalars, being optimized by the norm-bounding technique, are introduced for better handling the input and state constraints. The multistep approach, where a sequence of dynamic output feedback laws are optimized at each sampling instant, is given to improve the single-step approach. Numerical examples are given to illustrate the effectiveness of the proposed controllers.