Adaptive robotic arm control through digital twin integration and hybrid neural networks.

Modern robot arms that work in changing environments need to follow paths very accurately and adapt quickly when they face non‑linear disturbances, different loads, or wear. This research tackles those problems by combining a digital twin with a hybrid neural‑network controller. The digital twin is a virtual copy of the robot that runs in real time, constantly updating its state to match the real arm and predicting how the system will behave under different conditions. For better control, we use a hybrid neural network that mixes recurrent neural networks (which handle time‑dependent data) and feedforward neural networks (which map static non‑linear relationships). This network works together with standard feedback control

loops. It continuously estimates and cancels out unmodeled dynamics, friction, and external disturbances. The method gives a clear way to simulate, model, and test the closed‑loop stability and tracking accuracy of the robot arm. Simulation results show that linking the digital twin with adaptive neural control improves the arm’s transient response, reduces steady‑state error, and makes it more robust to parameter uncertainties. This framework provides useful guidance and tools for designing advanced control systems in industrial automation.

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