Human-robot physical interaction control method based on iterative optimal impedance

In order to improve the accuracy and compliance of human-robot physical interaction and achieve optimal interaction performance, a human-robot physical interaction control method based on iterative optimal impedance was proposed to solve the problem that iterative learning-based impedance control method needs to repeat the same task many times. The proposed method draws on the mechanism by which iterative optimal control can optimize cost function to determine optimal control input to the system without information of the system matrix. A double-loop control structure was used for the proposed control method. An iterative optimal impedance controller (IOIC) was designed for a task-oriented outer loop. The problem of determining optimal impedance parameters was described as a linear quadratic regulator problem, which utilized iterative optimal control to find optimal feedback gain and minimize cost function including tracking error and interaction force. Robot jitter caused by parameter mutations was avoided by introducing soft auxiliary functions. A nonsingular terminal sliding mode trajectory tracking controller (NTSMTC) was used in the inner loop of the robot to make the actual trajectory of the robot track impedance trajectory output by the outer loop, and the chattering of control law was eliminated by saturation function. Simulation results prove that the proposed method can obtain optimal impedance parameters only by using interactive information in the initial stage of the task once in a human-robot collaborative task, so as to minimize the trajectory tracking error and the force consumed by the human during the task.