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This paper is concerned with the clock synchronization problem for IEEE 1588 networks. First, the synchronization error is described as a bounded disturbance, and a linear extended state observer (LESO) is designed to estimate the lumped disturbance induced by the oscillator frequency drift and timestamps quantization errors. Then, the lumped disturbance is compensated by the proposed controller. The proposed approach has the advantage that it’s able to deal with non-Gaussian disturbance induced by accumulated quantization errors. Simulations are provided to validate the effectiveness and superiority of the proposed approach.

Irrigation system is a large scale system, consisting of many interacting channels, and spanning vast geographical areas. In practice, the water level is expected to the desired reference values for safety and efficiency. In this paper, the model of irrigation system is modeled by the principle on conservation of mass. Because of the strong coupling between neighboring pools in irrigation system and large disturbance from the environment, a non-cooperative distributed model predictive control (NDMPC) algorithm based on Nash optimality is proposed for the large scale irrigation system to design the controller for setting points regulation. Finally, a simulation example is given to demonstrate the effectiveness of the proposed algorithm.

Precise multi-axis operation is essentially required in orthodontics for tooth movement. Despite the development of flexible multi-dimensional force sensors that have effectively perceived multi-dimensional forces, they still face the challenge of simultaneously 3D force and moment in a single flexible force sensor. Six-dimensional force perception can successfully operate objects that critically rely on directional tracking and accurate monitoring of complex multi-axis stimuli. To realize the integration of sensing units with the perception of six-dimensional force under even a soft touch, we design a flexible six-dimensional force sensor with tenon-and-mortise interlocking structures inspired by traditional Chinese ancient architecture. This unique structure enables conjunction of deformation, which can be studied and decoded by Deep Neural Networks (DNN) with six-dimensional force, including forces and rotating moments in the x, y, and z directions. This soft sensor with minimal size (7 × 7 × 7 mm3) and high detection accuracy (the DNN error is below 10−4) can be used in orthodontic treatment for precise correction with a full collection of orthodontic force. This unique flexible six-dimensional force sensor provides a new strategy for the design of multi-dimensional force sensors, paving the way for the development of intelligent robotics, interactive human-machine interfacing, and advanced prosthetics.

Mimicry of the somatosensory system in the human skin via electronic devices exhibits broad applications in intelligent robotics and wearable electronics. Here, we report a novel biomimetic flexible dual-mode pressure sensor that is based on the interlocked piezoelectric and piezoresistive films with pyramid microstructures. The sensitivity of the sensor is significantly enhanced because of the induced larger stain variation along the thickness direction of the former piezoelectric film and increased contact area in the latter piezoresistive film. The synergistic effect of the piezoelectric and piezoresistive responses to stimuli also allows the dual-mode sensor to detect over broad pressure and frequency ranges. The analysis of these signals can deconvolute multiple aspects of the complex stimuli loading processes, including their loading direction, rate, magnitude, and duration. As a proof-of-concept demonstration, the dual-mode pressure sensor is successfully integrated with manipulators and human bodies to decode the complex and delicate picking processes and human motions, respectively. When combined with the other sensing modalities, the multifunctional dual-mode pressure sensor delivers new application opportunities in intelligent soft robotics and human-machine interfaces.