Predefined-time control (PTC) is employed to ensure that the system exhibits more definite and predictable behavior in the motor systems. However, as the system ages, sensors may be adversely affected by malfunctions, resulting in performance degradation, even leading to system instability. Thus, to solve this issue, a predefined-time sensor fault-tolerant control (PTSFTC) is developed for motor systems. First, a predefined-time fault compensation scheme is presented to extract the system operational state information. Then, this fault compensation method can judge the sensor fault within a predefined execution time. Second, a Nussbaum technique-based adaptive compensation strategy is introduced in PTSFTC to compensate for the unknown control coefficient. Then, the proposed PTSFTC can achieve safe and stable operation for motor systems with the unknown control gain. Third, the Lyapunov function is constructed to demonstrate the stability of PTSFTC scheme. Then, the comprehensive stability analysis can guarantee the successful application of PTSFTC. Finally, simulation and experiment results demonstrate that the PTSFTC can ensure the motor system exhibits excellent control performance.

Adaptive control (AC) has been extensively proved as a promising strategy for nonlinear systems, especially in the dynamic and changeable environments. However, due to the unavoidable existence of unknown interconnections in the real nonlinear systems, it is difficult for typical AC to guarantee its stability within the bounded convergence time. Thus, to solve this issue, a predefined-time adaptive neural control (PTANC) scheme is developed for nonlinear systems with unknown interconnections in this aritcle. First, an integrated control framework, where the controlled objectives not only relate with each other but also change over time, is able to catch more characteristics of nonlinear systems than the existing works. Then, the proposed control scheme can address the impact of unknown interconnections on the system stability. Second, a predefined adaptive law mechanism is employed to estimate the unknown interconnections to assist in PTANC. Then, the proposed PTANC scheme can ensure its predefined-time stable in the occurrence of unknown interconnections. Third, a neural network-based self-regulating strategy is designed to construct the Lyapunov function to prove the stability of PTANC. Then, the comprehensive stability analysis can make PTANC scheme be successful applied in nonlinear systems. Finally, the proposed PTANC scheme is tested on the numerical simulation and BSM1 simulation platform. The experimental results illustrate that the envisioned control method attains exceptional performance.

This paper focuses on the predefined-time adaptive neural tracking control problem for nonlinear multiagent systems (MASs). In contrast to the existing results of the predefined-time control methods, this paper introduces a lemma for achieving predefined-time stability within the framework of backstepping, and the primary distinguishing feature is the ability to predefine the convergence time according to user specifications and the controller design process being influenced by a singular parameter. Meanwhile, a numerical example is presented by using the proposed lemma such that the convergence performance can be ensured by the user practical specification. Moreover, by using the neural networks (NNs) and the finite time differentiators, an adaptive approach to predefined-time tracking control is presented for nonlinear MASs. This method ensures the predefined-time stability of all signals within the MASs, while also enabling the followers’ outputs to accurately track the desired trajectory with the predefined time. The effectiveness and merits of the proposed scheme are substantiated through simulation results.Note to Practitioners— This paper aims to address the predefined-time control problem for MASs, which can be widely used in practice, such as vehicular platoon systems control, teleoperation systems control, etc. The existing predefined-time methods only guarantee system convergence within the predefined-time interval, and achieving predefined-time convergence with an exact convergence time$t$remains a challenge. Moreover, the existing predefined-time methods contain many control parameters, which complicates the process of the parameter tuning. To address the aforementioned challenges, a predefined-time adaptive neural control method for MASs is developed, which can guarantee that all signals within MASs are predefined-time stable while enabling the followers to accurately track the desired trajectory with predefined time. Moreover, only one parameter and a pair of the finite time differentiators designed constants are involved in the controller design process, which simplifies the process of the parameter tuning.

Personalized venues recommendation is essential to help people find attractive venue to visit as growth of location-based social networks. Existing approaches never distinguish user individual interests from her social preferences, which leads to a bottleneck of modeling user check-in behaviors accurately. In this paper, we find the differences between user interests and her social preferences clearly and investigate the time law of user check-in behaviors in depth. Consequently, we propose a social-period-aware topic model (SPATM) to learn the influence weights of both user interests and her social preferences on making-decision for each check-in time automatically. Especially, we model latent topic by leveraging smaller size of dynamic activities instead of static categories, which can alleviate the data sparsity problem by using more co-occurrent activities information. Moreover, our approach can automatically judge whether a user’s social preference is periodic or aperiodic and learn the periodicity of periodic one. Furthermore, the Alias Sampling based training approach is introduced to improve sampling efficiency. The results demonstrate our proposed model is effective and outperforms the state-of-the-art approaches in terms of effectiveness and efficiency. Besides, SPATM can learn semantically coherent latent topics and geographically dispersed latent social topics which are useful to explain recommendation.

POI recommendation has become an essential means to help people discover attractive places. Intuitively, activities have an important impact on users’ decision-making, because users select POIs to attend corresponding activities. However, many existing studies ignore the social motivation of user behaviors and regard all check-ins as influenced only by individual user interests. As a result, they cannot model user preferences accurately, which degrades recommendation effectiveness. In this article, from the perspective of activities, this study proposes a probabilistic generative model called STARec. Specifically, based on the social effect of activities, STARec defines users’ social preferences as distinct from their individual interests and combines these with individual user activity interests to effectively depict user preferences. Moreover, the inconsistency between users’ social preferences and their decisions is modeled. An activity frequency feature is introduced to acquire accurate user social preferences because of close correlation between these and the key impact factor of corresponding check-ins. An alias sampling-based training method was used to accelerate training. Extensive experiments were conducted on two real-world datasets. Experimental results demonstrated that the proposed STARec model achieves superior performance in terms of high recommendation accuracy, robustness to data sparsity, effectiveness in handling cold-start problems, efficiency, and interpretability.