Compensation tracking control for a rehabilitation training walker considering data‐driven human–robot cooperative environment estimation

It is a challenge to realize the tracking control of rehabilitation walker under human–robot cooperation environment. In this context, a data‐driven model of human–robot cooperative environment is established, and a tracking controller is designed to compensate the influence of the motion environment on the tracking performance, so as to realize the trajectory and velocity tracking simultaneously and improve the tracking accuracy. By constructing the discrete model of the walker and the nonlinear relationship between the cooperative environment and the tracking error, the data‐driven model of the cooperative environment was obtained. By estimating the pseudo‐Jacobian matrix, the compensation controller was achieved, and then the influence of human–robot environment on the tracking motion of the walker was suppressed, so that the stable tracking training of the walker was obtained. The advantage of this design method is that the motion environment estimation does not depend on the mathematical model of the walker, so more accurate cooperative environment information can be obtained, and then it is possible to achieve accurate compensation tracking control. Simulation and comparative analysis were conducted, and results verified that the proposed compensation control method under the cooperative environment is very effective.