Research of a Self-adaptive Robot Impedance Control Method for Robot-Environment Interaction

Abstract

Abstract. The robot impedance control performance decreases with unknown or changing environmental stiffness and damping parameters, in order to resolve this problem, this paper designs a self-adaptive robot impedance control method, which is characterized by integration of off-line learning and on-line adjustment to afford the stiffness and damping of the robot control system's impedance model competent for unknown or changing environment. For the off-line learning, defining the robot impedance control performance criterion, and establishing the geometric representation of the varying stiffness parameter, we derive the initial values of stiffness for the impedance model. Further, a neural network is designed to estimate the environmental effective stiffness, and combined with critical damping condition of the second-order robot-environment interaction system, we solves the initial value of damping for the impedance model. During the on-line adjustment, a rule self-tuned fuzzy controller is dedicated to adjust the stiffness and damping of the impedance model based on robot real-time contact force and position feedback. At last, experiments demonstrate the excellent stability and accuracy for the robot-environment contact force tracking control.

Novel Voltage-Based Weighted Hybrid Force/Position Control for Redundant Robot Manipulators

“…Its control effect depends on the accuracy of the impedance model [12,13]. Initial parameters of the impedance model can be obtained by solving the minimization problem of the performance criterion and the critical damping condition of the robot-environment interaction system [14]. Uncertainties of the above problem can be considered by the optimization methods, such as robust optimization [15] or stochastic optimization [16].…”

“…Uncertainties of the above problem can be considered by the optimization methods, such as robust optimization [15] or stochastic optimization [16]. Then, the parameters can be adjusted online by using intelligent algorithms, such as neural network [14] or adaptive control [17], in practical applications. Consequently, the calculation of impedance control is considerably tedious, and its real-time performance is poor.…”