In this letter, we present a tracking control strategy for torque-driven joints to accurately execute the trajectory tracking of joints for a changeable task in an unstructured environment. This scheme incorporates the sliding-mode-based CTC, RBFNNs, and feedforward friction estimation, mainly consisting of two levels:1) feedforward level − we establish a new asymmetrical model for velocity-, load-, and temperature-dependent friction phenomena; 2) training level − multiple RBFNNs further estimate a joint system’s dynamic uncertainty and nonlinearity separately. Experimental results demonstrate that the proposed asymmetric friction model has a significant improvement in terms of friction compensation; the designed semiparametric scheme synchronously exhibits superior trajectory tracking performance in the joint space.However, this study does not consider the issues of fluctuated disturbances and input saturation. In future work, we will further optimize the control algorithm from the following two aspects:1) using a unified linear regression approach to identify dynamic parameters with the proposed friction model; 2) improving the sliding mode surface to ensure finite-time convergence of trajectory tracking errors. We will apply the optimized control algorithm to trajectory tracking of serial robots installed in the target scenarios.
