Neural-Network-Based Overshoot-Inspired Servo Motor Control for Industrial Robots

Unlike classical control theory, which involves analytical modeling of mathematical models for specific control objects, intelligent control organically combines analytical mathematical models with knowledge bases. On this basis, it constructs new control strategies through mathematical operations and knowledge reasoning. Among various intelligent algorithms, neural network control has gained increasing attention and application with the promotion and popularization of artificial intelligence. This control strategy connects numerous neurons with different weights to form a multi-layer network, utilizing different activation functions in each layer to adjust the output of the output layer, offering advantages such as good robustness and strong adaptive capability. However, due to the lack of interpretability in neural network control, its control stability is challenging to strictly demonstrate. Therefore, applying neural network control to industrial robot servo motor control carries uncontrollable risks, currently remaining at the laboratory verification stage and difficult to achieve true commercialization and generate economic value.

Addressing the bottleneck issue of “existing servo motor control struggling to meet high-precision control requirements in complex environments,” which restricts the development of industrial robots, this project aims to integrate advanced methods such as “advanced neural network intelligent algorithms” and “modern nonlinear stability theory.” It proposes a neural network speed stability compensation control method inspired by the motor overshoot problem to reduce speed overshoot during the dynamic response process of servo motors. This aims to improve the control precision of industrial robots while ensuring reliability. Subsequently, guided by the operational requirements for high-control precision servo motors used in high-end manufacturing, motor response speed, and smooth transition of speed under sudden load, the project will conduct theoretical research, technological innovation, and experimental verification from the perspective of motor nonlinear and strong coupling model mechanism analysis, overshoot pattern inspiration, neural network targeted design, and stability method derivation. Ultimately, it will establish a stable neural network speed control compensation algorithm design theory and hardware implementation architecture.