Guichao Yang

In the new era of intelligent manufacturing, renewable energy systems and other technological advancements, motor/hydraulic-actuator-based electromechanical servo systems have become a key foundation for obtaining high-performance control

This paper focuses on the output-constrained tracking control problem of magnetic drive transmission systems subject to modeling uncertainties. Specifically, a tracking error-based time-varying transformation function is introduced to convert the constrained system into an unconstrained framework. And radial basis function-based neural networks (RBFNN) will be employed to approximate the unknown nonlinear dynamics. Meanwhile, the extended state observer will be incorporated to estimate and compensate for external disturbances. The simulation results demonstrate the effectiveness of the proposed neuroadaptive learning algorithm in the presence of uncertainties.

This paper proposes a novel asymptotic secure tracking controller for motor servo systems. By introducing a novel asymptotic disturbance observer, the system uncertainties can be asymptotically estimated and compensated. Furthermore, by introducing a tracking error-based barrier function, it can achieve that the system output remains within the prescribed time-varying constraint boundaries. Additionally, a nonlinear asymptotic filter is incorporated into the design, which effectively circumvents the inherent “explosion of complexity” issue and achieves asymptotic tracking performance. Finally, the stability of the system is demonstrated through Lyapunov-based theoretical analysis, and the effectiveness of the proposed controller is validated by simulation results.

Micro-fabrication techniques are of great interest to many researchers owing to their high precision and design variability over large-area substrates. However, it often requires complicated, time-consuming, and costly processes, such as fabrication and etching for a highresolution photo-resist mask. Herein, we propose a rapid 3D micro-fabrication technique that utilizes a reusable freestanding hybrid mask-assisted electrospray technique. The highly convergent electric field created by the hybrid mask was embedded into an electrospray system to focus the charged Ag-NPs and form a micro-scale 3D structure with high efficiency. Numerical simulations and parametric studies were conducted. It is evident that the mask metallic layer dominates the generation of a highly convergent electric field, and the repulsive force provided by the insulating layer could prevent mask contamination. The final pattern resolution and aspect ratio could be effectively manipulated by controlling the mask voltage and deposition duration. The fabrication of a 3D structure with a height of 45 μm and an aspect ratio exceeding 3 only requires a deposition duration of approximately 60 s. The pattern resolution was 10 μm and the size ratio was less than 0.01. Furthermore, this technique has been applied to the fabrication of micro-scale heat sinks for thermal management in electronic devices.

In this study, a novel multilayer neuroadaptive reinforcement learning algorithm via the actor-critic mechanism will be proposed for tracking control of high-dimensional uncertain nonlinear systems. In order to compensate for the modeling uncertainties, the actor multilayer neural networks and the extended state observer based disturbance estimators will be incorporated via the command filtered backstepping to approximate endogenous uncertainties and external disturbances, respectively. Specifically, the critic multilayer neural network based reinforcement signals will be introduced to further improve the approximation performance of the actor multilayer neural networks and the tracking performance. The stability of the whole closed-loop system will be guaranteed by strict theoretical proof. Finally, the expected theoretical results will be verified.

A recurrent neuroadaptive output feedback controller which combines recurrent neural network (RNN) and extended state observer (ESO) will be proposed for motor servo systems with model uncertainties and signal noises. Notably, RNN can accurately approximate endogenous uncertainties. However, under strong external disturbances, its approximation performance may deteriorate and even instability may happen. Therefore, the controller in this paper will be proposed to handle these control issues. Simulation and experimental results demonstrate the effectiveness of the proposed controller.