In this paper, sliding mode tracking control and its chattering suppression method are investigated for flexible-joint robot manipulators with only state measurements of joint actuators. First, within the framework of singular perturbation theory, the control objective of the system is decoupled into two typical tracking aims of a slow subsystem and a fast subsystem. Then, considering lumped uncertainties (including dynamics uncertainties and external disturbances), a composite chattering-suppressed sliding mode controller is proposed, where a smooth-saturation-function-contained reaching law with adjustable saturation factor is designed to alleviate the inherent chattering phenomenon, and a radial basis function neural network (RBFNN)-based soft computing strategy is applied to avoid the high switching gain that leads to chattering amplification. Simultaneously, an efficient extended Kalman filter (EKF) with respect to a new state variable is presented to enable the closed-loop tracking control with neither position nor velocity measurements of links. In addition, an overall analysis on the asymptotic stability of the whole control system is given. Finally, numerical examples verify the superiority of the dynamic performance of the proposed control approach, which is well qualified to suppress the chattering and can effectively eliminate the undesirable effects of the lumped uncertainties with a smaller switching gain reduced by 80% in comparison to that in the controller without RBFNN. The computational efficiency of the proposed EKF increased by about 26%.

Nowadays, the commonly used yarn tension sensors cannot meet the requirement well in the textile manufacturing process. So surface acoustic wave (SAW) yarn tension sensor is a good selection to adapt to today's textile manufacturing and improve the production efficiency. First, two key problems of SAW yarn tension sensor in practical application are proposed in this article, one is the construction of the sensor measurement system, the other is the temperature compensation. Then, the integrated design of measurement circuit and measurement structure is implemented and introduced in this article, which improves the accuracy, reliability, and sensitivity of SAW yarn tension sensor. Finally, binary regression analysis method and least squares support vector machine (LSSVM) algorithm optimized by particle swarm optimization (PSO-LSSVM) method are applied to the temperature compensation of SAW yarn tension sensor, which both achieve satisfactory compensation effect. By comparison, PSO-LSSVM method is more convenient and flexible, and still has a large space for improvement. Through those research contents, it can be concluded that the design and implementation of the measurement system lay a foundation for the practical application of SAW yarn tension sensor, the research of temperature compensation is of great significance to the wide application of the sensor.

Personalized decentralized federated learning (PDFL) has emerged recently to address the problem of single point of failure and data heterogeneity in traditional centralized federated learning. However, existing works on PDFL still have two challenges that urgently need to be solved. First, model updates exposed by point-to-point communication during collaborative training in PDFL may disclose sensitive information about clients. Second, the distributed structure makes PDFL vulnerable to Byzantine attacks, which can disrupt the network by introducing poisoned data or faulty behaviors. In this article, we propose a privacy-enhanced and Byzantine-resilient approach to effectively address the dual challenges of privacy and security in PDFL. In particular, each client is required to build a unique critical parameter index set by evaluating the importance of its model parameters and broadcasting it to neighbors. To improve Byzantine resilience, we propose a novel weight allocation scheme based on the critical parameter index set for clients to alleviate the negative impact of Byzantine neighbors in the model aggregation. To enhance privacy protection while boosting personalization, we combine with model decoupling and execute a clipping-robust personalized local training for each client to achieve user-level differential privacy. We finally conduct exhaustive experiments on FEMNIST, SVHN, and CIFAR10 datasets and various settings. Experimental results demonstrate that compared to five state-of-the-art baselines, our proposed method achieves excellent performance with user-level differential privacy guarantee in PDFL and implements additionally superior Byzantine robustness in adversarial settings.

Federated learning (FL) enables clients to learn a machine learning model collaboratively without sharing their private local data to the server. However, due to its distributed structure, FL is vulnerable to poisoning attacks where adversaries intentionally send the poisoned local model parameters to the server and further affect the behavior of the global model. Existing works on mitigating poisoning attacks in FL are difficult to accurately characterize data relationships of high-dimensional parameters. Moreover, they cannot guarantee Byzantine robustness when the majority of clients are compromised (Byzantine ratio) and clients’ local datasets are highly non-independent and identically distributed (non-IID). In this paper, we conduct a pioneering work to introduce deep one-class classification into mitigating poisoning attacks in FL, which can guarantee Byzantine robustness even under a Byzantine ratio and non-IID degree greater than 0.5 with the assistance of a root dataset. Our key idea is to adequately learn vector features of benign local model parameters using Deep Support Vector Data Description (Deep SVDD) and achieve the optimal classification by training a deep learning-based one-class classifier equipped with a proper decision boundary based on the root dataset. To further optimize the classifier, we employ a regularizer based on random noise injections, which can address the hypersphere collapse problem inherent in Deep SVDD. Exhaustive experiments on MNIST, F-MNIST, and CIFAR-10 demonstrate that compared with five typical Byzantine-robust methods, our defense strategy achieves excellent effectiveness in mitigating targeted/untargeted poisoning attacks and an adaptive attack in FL. Even with a similar but different root dataset, it still maintains a good Byzantine robustness.

This article proposes an approach using surface acoustic wave (SAW) device to decrease passband ripples and sidelobes as two key problems of wavelet transform processors (WTPs). The passband ripples are primarily generated through electrode reflections and transducer end-effects. The proposed method uses unbalanced split-electrode interdigital transducers (IDTs) to prevent electrode reflections. The problem of transducer end-effects is alleviated by attaching SAW absorbers to both ends of the substrate edge, which can effectively absorb the spurious SAW arriving at the edges of the piezoelectric substrate. Moreover, because the radiation energy of bulk acoustic wave (BAW) has a sidelobe distribution, the greater sidelobes are predominantly caused by the BAW radiation. This problem is alleviated by engraving bidirectional slots on the substrate at the back of the WTPs of the unbalanced split-electrode IDTs to suppress the BAW radiation. The experimental results demonstrate that the passband ripples and sidelobes of the WTPs decrease to 0.72 and 6.58 dB, respectively.