Abstract
Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.
Highlights
I N RECENT decades, there has been a pronounced tendency to focus the studies of coordinated dual-arm robots in robotics and automation communities [1]–[5]
We develop a dual-arm robot control scheme by using the approximate Jacobian matrix (AJM) technique and the adaptive fuzzy logic system (FLS), such that the robot can be well controlled in the absence of robot dynamics and kinematics
The main contributions of the proposed control scheme could be summarized as follows: 1) constructing an adaptive fuzzy logic control scheme for the coordinated robot arms with neither a priori knowledge of system dynamics nor information of the kinematic parameters; 2) designing a novel parameters adaptation framework by applying a set of auxiliary filtered matrices, such that the parameter estimation errors could be appropriately expressed without using the robot joint accelerations; 3) relaxing the persistent excitation (PE) condition by introducing the concepts of the partial persistent excitation (PPE) and spatially localized approximation (SLA) of the FLS, such that the weights could converge to their optimal values when tracking a periodic trajectory
Summary
I N RECENT decades, there has been a pronounced tendency to focus the studies of coordinated dual-arm robots in robotics and automation communities [1]–[5]. Dynamic uncertainties of the dual-arm robot widely exist as well, let alone various unknown factors of the operating environment and the objects under manipulation These uncertainties may cause degeneration of the control performance or even incur instable system states. The main contributions of the proposed control scheme could be summarized as follows: 1) constructing an adaptive fuzzy logic control scheme for the coordinated robot arms with neither a priori knowledge of system dynamics nor information of the kinematic parameters; 2) designing a novel parameters adaptation framework by applying a set of auxiliary filtered matrices, such that the parameter estimation errors could be appropriately expressed without using the robot joint accelerations; 3) relaxing the PE condition by introducing the concepts of the PPE and spatially localized approximation (SLA) of the FLS, such that the weights could converge to their optimal values when tracking a periodic trajectory
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