Abstract
Data detailed on robotics have been pr esented. According to news originating from Montreal, Canada, by NewsRx correspo ndents, research stated, "This paper designs a minimal neural network (NN)-based model-free control structure for the fast, accurate trajectory tracking of robo tic arms, crucial for large movements, velocities, and accelerations." Funders for this research include Institut De Valorisation Des Donnees. Our news reporters obtained a quote from the research from Polytechnique Montrea l: "Trajectory tracking requires an accurate dynamic model or aggressive feedbac k. However, such models are hard to obtain due to nonlinearities and uncertainti es, especially in low-cost, 3D-printed robotic arms. A recently proposed model-f ree architecture has used an NN for the dynamic compensation of a proportional d erivative controller, but the minimal requirements and optimal conditions remain unclear, leading to overly complex architectures. This study aims to identify t hese requirements and design a minimal NN-based model-free control structure for trajectory tracking. Two architectures are compared, one NN per joint (INN) and one global NN (GNN), each tested on two serial robotic arms in simulations and real scenarios. The results show that the architecture reduces tracking errors ( RMSE <2°)."
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