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Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes.

An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error.

Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme.

An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

This study analyzed Chinese higher education (HE) governance policies during the first four months of the 2020 COVID-19 pandemic, providing insight into HE management, policymaking and governance. This study also illuminates Chinese student experiences and real-time policy impact, providing insight into the effectiveness of HE crisis management.

Using frameworks of real-time policy evaluation, this paper analyzed theoretical and realized policy impacts through the theoretical framing of temporality. Using real-time evaluation methods, this paper first identified HE policy priorities and then used a mixed-methods approach of “policy as discourse” analysis and a quantitative survey from Chinese HE students to assess the theoretical and realized impact of policies.

An analysis of HE policies identified three priorities: pastoral care, graduate employment and ideological education. Discourse analysis revealed each priority of HE policies was intrinsically linked to mitigating societal, economic and political consequences of the epidemic. Survey data revealed the perceived effectiveness of policies mirrored China's top-down government structure. Additionally, students expressed strong support in the central government's crisis management, despite relatively little realized policy impact.

This paper presents a timely review of HE governance during a global pandemic by offering a snapshot of HE crisis management and contributing to the literature surrounding China's ongoing HE centralization. This paper also provides unique insight into HE's role in state development, variations between prescriptive and realized policy impact and the “crisis as opportunity” paradox in a contemporary setting.