Abstract
For solving uncertainties of robotic dynamics and improving system robustness, an adaptive neural network (NN) tracking control is proposed considering uncertainties of robotic dynamics. Firstly, the kinematic model and dynamic model of robots are addressed. When the dynamics of the robots are known, a model-based tracking control strategy is proposed. Then, considering that the robotic dynamics are unknown, an adaptive radial basis function (RBF) neural network tracking control is proposed based on full state feedback to solve uncertainties. Disturbance observer is designed to counteract to unknown disturbance. By utilizing the Lyapunov direct method and the back-stepping method, all error signals are shown to be semi-globally uniformly bounded (SGUB). By choosing proper parameters, the tracking error can converge to a small neighborhood of zero. Simulation results and experiment results on Baxter robot are carried out to show the effectiveness of proposed method.
| Translated title of the contribution | Disturbance Observer-based Adaptive Neural Network Tracking Control for Robots |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 1307-1324 |
| Number of pages | 18 |
| Journal | Zidonghua Xuebao/Acta Automatica Sinica |
| Volume | 45 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2019 |
| Externally published | Yes |