Robust Adaptive Control of a Bimanual 3T1R Parallel Robot With Gray-Box-Model and Prescribed Performance Function

An elaborated bimanual 4-degrees-of-freedom (DOF) parallel robot is designed with large translational workspace, light moving mass and direct-drive actuation. The transient response greatly affects the throughput when using this robot for high-speed pick-and-place applications. However, this cannot be properly taken care by existing black-box model-based control schemes. Meanwhile, it is tedious to derive its dynamic model analytically and such a model is inapplicable for the real-time control. In this work, a gray-box-model-based control structure is proposed, with the retained inertial dynamics are directly derived by the principle of virtual work and the other parts are estimated by adaptive neural networks. This ensures calculation efficiency and integrity of the dynamics. Moreover, a prescribed performance function is constructed to ensure the specified tracking requirements both in transient and steady states. On the basis, the robust integral of signum of error term is incorporated to compensate the structural and unstructural uncertainties, which further improves the robustness during high-frequency motion. Comparative real-time experiments have been performed on the actual robot with attainment of the predefined performance and higher tracking accuracy.