Event-Triggered Adaptive Neural Impedance Control of Robotic Systems

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

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.