Composite Learning Variable Impedance Robot Control With Stability and Passivity Guarantees.

Variable impedance control (VIC) is paramount for robots to improve safety and effectiveness in physical human-robot interaction. However, achieving variable target impedance with guaranteed stability is not trivial, particularly under parametric uncertainty in the robot dynamics. This letter proposes a composite learning-based VIC (CL-VIC) strategy with three control modes, including robot-dominant, human-dominant, and collaboration modes, to achieve target impedance under parametric uncertainty. A multi-mode adaptive control scheme is defined by well-designed weighting functions to ensure smooth mode transitions. A composite learning mechanism is employed for exact parameter estimation such that target impedance models can be achieved under interval excitation that is much weaker than persistent excitation, where the latter is a sufficient condition for exact parameter estimation in classical system identification. The proposed method can achieve variable active stiffness and damping with guaranteed exponential stability and passivity of the closed-loop system, which ensures safe interaction. Experiments on a collaborative robot with seven degrees of freedom named Franka Emika Panda have validated the effectiveness of the proposed CL-VIC method.