In this presented paper, the trajectory tracking control is considered for an autonomous underwater vehicle subjected to model uncertainties and output constraints. The tan-type barrier Lyapunov function is adopted to deal with the requirement of output constraints. In order to cope with the existing system uncertainties, the adaptive fuzzy is employed to approximate the unknown system parameters of an autonomous underwater vehicle. Both state feedback tracking controller and output feedback tracking controller are proposed in this work. The state feedback tracking control scheme is designed by comprehensive application of tan-type barrier Lyapunov function and adaptive control design provided that all system states are measurable. The output feedback tracking control scheme is proposed by employing a high-gain observer. Through analysis, it can be shown that the designed control laws are able to achieve the desired output constraints. In addition, all the signals of the resulting closed-loop system are semiglobally uniformly bounded. Finally, simulation is carried out to demonstrate the validity and feasibility of the designed control strategies.

The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an $n$ -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

In this article, a new stabilization control strategy is designed for a kind of nonholonomic system under full-state constraints and external disturbances. First, a stabilization controller for the first input is proposed to divide the entire control process into two stages. Then, using state transformations, the remaining original system is converted into an unconstrained system for which the stabilization controller is designed based on dynamic surface control. Stability analysis has established that the developed stabilization control algorithm can achieve the desired stabilization control objectives and the predefined full-state constraints. Meanwhile, all closed-loop signals keep bounded in the whole control process. Simulation and experiment results are given finally to demonstrate the effectiveness of the proposed stabilization control algorithm.

This paper studies the complete state tracking control of an underactuated ship with only two controls, namely, surge force and yaw moment. A novel finite-time switching controller is developed guaranteeing that the ship can track a reference trajectory generated by a virtual ship. The solution is based on the inherent cascaded interconnected structure of the ship dynamics. As opposed to the previous works, the assumptions used in this work are more relaxed in the sense that the reference surge and yaw velocities are allowed to be zero at the same time. Simulation results are given to serve the purpose of demonstrating and validating the proposed tracking methodology.