Output Finitely Constrained Control of Robotic Manipulators With Composite Desaturation Method

This article explores the challenges of input saturation and output constraints in the context of a dynamic tracking task for robotic manipulators, considering model uncertainties and the absence of velocity measurements. First, a time-varying global barrier Lyapunov function (GBLF) is designed without restrictions on its application to achieve finite output constraints. Subsequently, an adaptive boundary mode and a new auxiliary system are proposed as part of a composite desaturation scheme to address input saturation. The adaptive boundary mode adjusts GBLF-related control signals while the auxiliary system generates compensation signals for each order subsystem for desaturation. Then, an extended state observer and a radial basis function neural network are employed to estimate velocities and lumped uncertainties, respectively. Finally, a novel controller integrating the aforementioned techniques is derived, achieving satisfactory convergence for each processor of the controller. The advantages of this controller lie in its effective resolution of existing BLFs' applicability issues and noticeable improvement in desaturation rate. Experiments validate the effectiveness and superiority of the proposed method.