Proportional-Integral Approximation-Free Control of Robotic Systems With Unknown Dynamics
Chao Zhang, Jing Na, Jiande Wu, Qiang Chen, Yingbo Huang
Abstract
This article presents a novel proportional-integral approximation-free control (PIAFC) for nonlinear robotic systems with unknown Coriolis and gravity dynamics. One key merit is to transform the original motion tracking problem into an alternative system stabilization problem by introducing prescribed performance functions (PPFs) and the associated error transformation. Another idea is to develop a proportional-integral error compensation mechanism and incorporate it into the approximation-free control synthesis, such that the tracking error converges to zero in the steady-state. In this framework, only the inertia matrix is required and the computationally demanding function approximators are avoided, while the unknown Coriolis and gravity dynamics can be effectively handled. Moreover, the transient tracking error can be retained within a preset boundary. Stability analysis of the closed-loop control system is carried out in terms of the Lyapunov theorem. Finally, extensive comparative simulations and experimental results on a realistic SCARA robotic test-rig illustrate the superiority of the suggested method over several other methods.