Most of the existing identification/control algorithm of uncertain robot manipulators have been proposed to achieve model identification and trajectory tracking with expected precision, but the convergence time and transient tracking performance have been rarely discussed. In this paper, an adaptive fixed-time estimation algorithm is proposed for an uncertain robot. A recursive update law combined with an auxiliary filtering technique has been exploited such that the measurement of acceleration signals could be avoided during the estimation process. Based on the results of parameter identification, we propose a fixed-time control scheme which can guarantee the specified motion performance and prescribed convergence time simultaneously. The tiny practical error of parameter identification can be effectively handled with the proposed control scheme. Finally, the simulation results based on an uncertain 2-DOF robot have demonstrated the effectiveness of the proposed identification/control algorithm.
Most of the existing identification/control algorithm of uncertain robot manipulators have been proposed to achieve model identification and trajectory tracking with expected precision, but the convergence time and transient tracking performance have been rarely discussed. In this paper, an adaptive fixed-time estimation algorithm is proposed for an uncertain robot. A recursive update law combined with an auxiliary filtering technique has been exploited such that the measurement of acceleration signals could be avoided during the estimation process. Based on the results of parameter identification, we propose a fixed-time control scheme which can guarantee the specified motion performance and prescribed convergence time simultaneously. The tiny practical error of parameter identification can be effectively handled with the proposed control scheme. Finally, the simulation results based on an uncertain 2-DOF robot have demonstrated the effectiveness of the proposed identification/control algorithm.