Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID

Abstract

Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance.

Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot

“…Active force control is the use of designed control strategies to actively control force through the feedback of contact force information [32]. In the aspect of force control algorithms, the design of the force controller is the most important, and the common force control algorithms are impedance control [33,34] and hybrid position/force control [35,36]. For uncertain environments, that is, when the stiffness and position of the environment are unknown, it is difficult to obtain an accurate model, which makes it difficult to achieve high-precision force tracking.…”