ESO-Based Safety-Critical Control for Robotic Systems With Unmeasured Velocity and Input Delay

For practical robots, obtaining precise dynamic models and states is a challenge, which presents difficulty in achieving safety-critical control. When faced with an uncertain dynamic model of the robotic system and the absence of measurements for joint velocity, this article proposes a method by combining extended state observer (ESO) and control barrier function (CBF) for safety-critical control. Firstly, an ESO is used to estimate the model and states in real time. Then, according to the estimation error, the ESO-based CBF (ESO-CBF) is proposed, and a quadratic programming subject to ESO-CBF is constructed to calculate the control input for robotic systems. In addition, input delay is also considered for robotic systems with uncertain models. In cases involving input delay, a predictive ESO is designed to estimate the model, and the corresponding estimation error boundary is derived. Based on the estimation error, ESO-CBF is constructed to ensure the safety constraint. Finally, the effectiveness of the proposed method is verified by the obstacle avoidance task of Franka Emika Panda manipulator.