Accurate alignment via an open-loop nonlinear mapping strategy for a tri-axial hybrid serial-parallel robot

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Abstract This paper presents a calibration-based open-loop method to improve alignment accuracy for a cost-effective and posture-adaptive three degree of freedom (3-DOF) hybrid serial-parallel robot. Instead of using expensive high-dynamic-accuracy sensors for continuous closed-loop alignment, the proposed open-loop strategy requires only static-state measurements. The mapping between motor pulse counts and end-effector poses is modeled as a multiple-input multiple-output (MIMO) nonlinear system, with parameters identified to minimize input–output errors. Identification of the nonlinear system is transformed into a linear least-squares problem via the Kronecker product. A noise-interference analysis provides guidance for sensor selection to ensure reliable calibration under practical conditions. Real-time experiments show that the proposed calibration approach significantly reduces attitude errors, achieving root mean square error (RMSE) reduced to 2%, 8%, and 6% of the uncalibrated system for pitch, roll, and yaw, respectively. In addition, the pitch-axis error is reduced to 40% of that obtained with linear-model calibration.

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