Robust Adaptive Control for Robotic Systems with Guaranteed Parameter Estimation

In most adaptive control systems with unknown parameters, the adaptive laws are usually designed based on the predictor/observer error or control error. In this paper, we propose a novel adaptive control scheme for nonlinear robotic systems by incorporating the parameter error into the adaptive law, which leads to guaranteed estimation performance. By carrying out filter operations, the robotic system can be linearly parameterized without using the measurements of acceleration. Then a new adaptive algorithm is introduced to guarantee that the parameter error and control error exponentially converge to zero. In particular, we provide an intuitive method to online verify the standard PE condition used for the parameter estimation. The robustness against disturbances is also studied and appropriate comparisons to several adaptive laws are provided. Simulations with a realistic robot arm are presented to validate the improved performance.