Avoiding the usage of joint accelerations during robot online identification and control is significant in improving modeling and tracking accuracy. Regressor filtering is feasible to achieve acceleration-free online identification, where the robot dynamics is linearly parameterized and filtered to obtain an acceleration-free filtered regressor. Nevertheless, existing calculation methods of the filtered regressor are either applicable only to robots with low degrees of freedom (DoFs) or restricted by robot modeling techniques. We propose an acceleration-free analytical regressor filtering (AF-ARF) method to obtain the filtered regressor without restrictions on the number of DoFs or robot modeling techniques, where joint accelerations are eliminated by integration by parts, and the filtered regressor is derived by using the skew-symmetric property of the inertia matrix and some matrix operations. An acceleration-free composite learning robot control strategy based on AF-ARF is developed for exact online identification and control, where closed-loop exponential stability with parameter convergence is established under a weakened condition of interval excitation. Simulative and experimental comparisons based on a seven-DoF industrial robot have validated the superiority of our method over state-of-the-art methods in online identification, model prediction, and tracking control under reduced computing burden.

To enhance robustness against noise resulting from velocity measurement and acceleration estimation in robot online identification and adaptive control, the robot dynamics should be filtered and parameterized to generate a filtered regression matrix regarding identifiable parameters. However, generating a filtered regression matrix is complicated for robots with high degrees of freedom (DoFs). The power balance model (PBM) of robots with spatial notations stands out as an effective option for online applications owing to its simplicity in generating an easily computed and acceleration-free filtered regression vector. This paper proposes a PBM-based recursive composite learning robot control (RCLRC) method to enhance parameter convergence so as to boost tracking control. Based on the PBM, a filtered regressor with a computational complexity of O(n) (instead of O(n2) to O(n4) for its dynamic model-based counterpart) is employed to calculate an excitation matrix, and a generalized regression equation for composite parameter update is normalized to provide more uniform convergence rates across all parameter components. Experiments on a 7-DoF robot manipulator have shown that the proposed PBM-RCLRC outperforms state-of-the-art methods on parameter estimation and tracking control.

Parameter convergence in adaptive control is crucial for improving the stability and robustness of robotic systems. Nevertheless, a stringent condition named persistent excitation (PE) needs to be satisfied to ensure parameter convergence in the conventional adaptive robot control. Composite learning robot control (CLRC) is an innovative methodology that guarantees parameter convergence under a condition of interval excitation (IE) that is strictly weaker than PE. This paper puts forward a time-division multi-channel (TDMC) CLRC strategy such that parameter convergence is achieved even without the IE condition. In the TDMC mechanism, a filtered regressor is integrated with multiple time intervals to generate a generalized prediction error for parameter update, such that excitation information of regressor channels at different instants is exploited more effectively and efficiently to achieve fast and accurate parameter estimation. Global exponential stability with parameter convergence of the closed-loop system is achieved under a partial IE condition that is much weaker than IE. Experiments on a collaborative robot with 7 degrees of freedom have demonstrated the superiority of the proposed approach in both parameter estimation and trajectory tracking compared to start-of-the-art approaches.

Cartesian impedance control plays a significant role in improving the safety and compliance of robot end-effectors when executing collaborative tasks with humans or environments. However, achieving target impedance is challenging under uncertain robot dynamics. In this study, we raise a composite learning-based Cartesian impedance control method to ensure exact Cartesian trajectory tracking in free motion and Cartesian target impedance in interaction under uncertain robot dynamics. Introducing a composite learning update to precise robot modeling online, so the exponential convergence and passivity of the closed-loop robot dynamics are guaranteed under a weak condition known as interval excitation. The efficacy and superiority of this method have been demonstrated through experiments on a collaborative robot with 7 degrees of freedom known as Franka Emika Panda.