Contents I. Introduction
As one of the main compliance control techniques, impedance control (IC) integrates the Cartesian space trajectory and contact force of the robot’s end-effector into one framework, which can prevent the problem resulting from the separate control in the orthogonal space of position and force. Therefore, since the concept of IC was first proposed by Hogan [1], IC for robotic manipulator has been widely studied, such as robust IC [2], [3], [4], [5] and hybrid IC [6], [7], [8], [9], [10]. Since the adaptive IC (AIC) does not require the accurate parameter information of the system and environment, which makes the controller design easier, different types of AIC [11], [12], [13], [14], [15], [16], [17], [18] have been proposed for robotic manipulator. Sharifi et al. [12] proposed four model reference adaptive impedance controllers by linearly parameterizing the robotic system. Peng et al. [16] designed an adaptive neural position/force tracking IC strategy for the robotic system, where the neural network (NN)-based adaptive compensator was used to solve the system uncertainties. To realize the target impedance model, Yu et al. [18] used the AIC strategy and a Bayesian scheme to obtain the human impedance parameters and human motion intention recognition. Chien and Huang [19] designed the function approximation technique-based AIC scheme to prevent the computation of the regressor matrix.
