IEEE Transactions on Cybernetics最新文献

Feature selection is one of the hot issues in machine learning. It reduces storage pressure by effectively screening features and has become a very practical data preprocessing method. At present, most feature selection algorithms apply $ell _{2,1}$ -norm on the transformation matrix to calculate the scores for all features and then select appropriate features according to these scores. But their sparsity is limited, and meaningless regularization parameters increase the cost, making it prone to falling into local optimum. To solve the above difficulties, this article proposes a novel max-min robust unsupervised feature selection via sparse subspace (MMRUFS), which considers both the reconstruction term and variance term of data, so that the model can not only fully retain the original information of data, but also make the data more dispersed. Second, $ell _{2,0}$ -norm constraint is used on the transformation matrix to directly select the optimal feature subset, avoiding the fine-tuning of regularization parameters. To enhance the robustness, MMRUFS carefully designs mark weight vector to make the model treat normal samples and outliers differently and achieves the effect of anomaly detection. Finally, MMRUFS is solved by designing the surrogate matrix, and its convergence is strictly guaranteed, experimental results reveal that MMRUFS outperforms other feature selection algorithms on multiple real-world datasets.

Sensor drift, which is the deviation of measurements over time, can compromise controller performance and cause system instability. To address this challenge, this article proposes a proactive fault-tolerant control strategy for distributed parameter systems. The proposed strategy is based on a time-varying spatiotemporal model that captures system dynamics. The initial phase of this research involves designing an adaptive observer-based detector to identify the temporal and spatial locations of fault occurrences accurately. Subsequently, a joint state-and-fault estimator is developed to accurately reconstruct the fault profile, even in the presence of strong state-fault coupling. The controller provides real-time corrections based on the estimation results. A rigorous stability analysis of the closed-loop system is provided, and the effectiveness of the controller is validated through experiments involving two distinct fault scenarios.

Humans can naturally learn and adapt to walking patterns in a variety of terrains. To simulate this learning characteristic, this article introduces a neural dynamics-based impedance optimization and trajectory adaptation approach for our designed soft exosuit, with a dual-driven configuration to assist both ankles of individuals. This method adaptively learns the impedance of the human ankle joint using measured interaction forces and dynamically adjusts trajectories to align with real-time human-robot interaction. Additionally, an adaptive control framework integrating neural dynamics-based optimization with several adaptive laws is developed to achieve stable tracking of updated reference trajectories, with Lyapunov stability analysis confirming uniform ultimate boundedness (UUB) of the closed-loop system. The designed controller offers the benefit of concurrently addressing trajectory adaptation, force control, and impedance tuning for soft exosuits. Experimental validation on human subjects across various terrains demonstrates that the proposed method reduces maximum trajectory tracking error to 0.016 rad (lower than PID and ADRC controllers) and enables impedance parameters to converge within 3 gait cycles. The controller concurrently addresses trajectory adaptation, force control, and impedance tuning, offering a lightweight (8 kg) and wearability-optimized solution for walking assistance.

The repetitiveness prerequisite of iterative learning control has always been the main obstacle to promoting its practical applications. In this article, a novel adaptive iterative learning reliable control scheme is proposed for the nonrepetitive systems with multiple iteration-varying parametric uncertainties, where actuator faults and state delays are considered simultaneously. During the design of the controller, the class- $k_{infty } $ function is leveraged to dispose of the unmodeled lumps of systems through neural networks, and the transformation of control signals is established to compensate for the negative impact of the inefficient actuator. The technical features of our approach lie in an innovative parametric estimation mechanism that integrates the hyperbolic tangent function and an auxiliary sequence is presented to accommodate the nonrepetitive uncertainties, thus achieving the zero-error convergence of output. As the main merits, the proposed control scheme is promising to manifest better performance and practicality than the existing methods, owing to the weak assumptions on the system dynamics, the little prior knowledge of parametric uncertainties, and the strong learning ability of the controller.