Integral-Enhanced Hierarchical Control for Redundant Manipulators With Unknown Kinematics

The negative impact of noises is inevitable in the learning and control processes of redundant manipulators, which may degrade the performance. To mitigate challenges posed by noises, this paper proposes an integral-enhanced hierarchical control and kinematics learning (IHCKL) algorithm. This algorithm comprises two components: an integral-enhanced kinematics learning (IKL) model that incorporates an integral feedback term to reduce the influence of noises during kinematics learning, and an integral-enhanced hierarchical control (IHC) model that combines neural dynamics with hierarchical control to improve the control accuracy and noise resistance in noisy environments. The proposed algorithm accurately learns the kinematics in noisy environments, while achieving multi-task execution with priorities. Different from existing state-of-the-art algorithms that are difficult to achieve multi-task executions under unknown kinematics and noisy conditions, the IHCKL algorithm integrates both kinematics learning and hierarchical control with noise resistance, exhibiting enhanced robustness and accuracy. Theoretical analyses demonstrate that the IHCKL algorithm guarantees the error convergence and noise resistance under varying noise conditions. Simulations and experiments confirm that the IHCKL algorithm offers improvements in the multi-task execution, kinematics learning, and noise resistance in noisy environments.