Guidelines to design a neural network as a feedforward controller for fast trajectory tracking of robotic arms

Abstract

Abstract Tracking fast and accurate trajectories of robotic arms can be important in applications involving large movements, velocities, and accelerations. This would require either an accurate dynamic model of the arm or an aggressive tracking with high-gain feedback. Concretely, it can be difficult to obtain the accurate model, due to nonlinearities and uncertainties. Current developments of 3D-printed and low-cost robotic arms accentuate this issue. Control architectures for high-speed trajectory tracking requiring no dynamic model were recently proposed. These consist in learning the dynamic response of a proportional derivative controller with a neural network (NN) as feedforward controller. However, no detail was provided to make the most of these architectures. This paper aims to provide guidelines for an optimal design of a neural network (NN) as feedforward controller for fast and accurate trajectory tracking of robotic arms. The subsequent objective is to compare 1. one NN per individual joint (INN’s method); and 2. one global NN (GNN method). The method compares these two architectures. Results are illustrated with two serial robotic arms of 3 and 5 degrees of freedom, simulated then in reality. The main results are as follows: The control architecture reduces the trajectory tracking errors (RMSE < 2°). The INN’s method can be used when the joints dynamics are decoupled and requires less data than GNN method to learn the dynamics. A table sums up the guidelines for design, in five main steps. Perspectives are to apply these guidelines to develop low-cost robotic arms and extend to micro-movements.