Abstract
Most existing adaptive neural network (NN) robot control methods suffer from poor convergence performance of network weights, which cannot fully exploit the approximation ability of NNs. A sufficient condition to ensure parameter convergence of NNs in these control methods is termed persistent excitation, which is difficult to be satisfied in practice. In this paper, an adaptive echo state network (ESN) robot control method enhanced by composite learning is proposed for a trajectory tracking problem of robotic arms with multiple degrees of freedom (DoFs), where a chaotic ESN is used to accurately model and compensate for robot uncertainty online. In the proposed method, a generalized prediction error is defined based on memory regressor extension, and both the prediction error and the generalized prediction error emerge into the weight update law of ESNs such that exponential convergence of parameter estimation, implying exponential convergence of trajectory tracking, is guaranteed under a weaker condition termed interval excitation. Simulation results based on a 7-DoF collaborative robot have verified the effectiveness and superiority of the proposed method.
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Acknowledgements
This work was supported in part by the Guangdong Pearl River Talent Program of China under Grant No. 2019QN01X154, and in part by the Fundamental Research Funds for the Central Universities of China under Grant No. 19lgzd40.
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Wu, R., Li, Z., Pan, Y. (2021). Adaptive Echo State Network Robot Control with Guaranteed Parameter Convergence. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13016. Springer, Cham. https://doi.org/10.1007/978-3-030-89092-6_53
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