Efficient Impedance Controller Tuning of Robot–Environment Interaction With Improved Transient and Steady-State Performance

Abstract

This article proposes an efficient tuning strategy for impedance controllers based on multiobjective Bayesian optimization to achieve accurate force tracking in robot-environment interaction tasks. Contrasting with the single-objective optimization-based tuning methods, we fully exploit the force feedback information to construct objective functions that characterize both the transient and steady-state behaviors, thereby enabling a joint optimization of the response performance of the interaction force. The proposed method employs Gaussian processes (GPs) as surrogate models for the objective functions. Following the approximation of the Pareto frontier using the nondominated sorting genetic algorithm II (NSGA-II), impedance parameters are adjusted based on the Hypervolume indicator. We compare our proposed method with three mainstream impedance controller tuning strategies and three multiobjective optimization-based tuning methods, demonstrating its superiority in tuning efficiency and interaction performance. Furthermore, the proposed method is experimentally verified using a self-developed robotic manipulator across various interaction environments. The method realizes efficient and high-performance controller tuning via only a handful of interaction attempts. Finally, the robustness and convergence of the proposed method are discussed in detail to promote its applicability in real-world interaction control.