Adaptive Parameter Estimation and Control Design for Robot Manipulators with Finite-Time Convergence

For parameters identifications of robot systems, most existing works focused on the estimation veracity but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to traditional methods, information of parameters estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, Newton-Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition based model reduction (SVD-MR) method was designed to remedy the potential singularity problems of the NE regressor. Moreover,an interval excitation (IE) condition was employed to relax the requirement of persistent excitation (PE) condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time identification algorithm based on a 7 degree of freedom (DOF) arm of Baxter robot.