A model-assisted ESO-based nonlinear ADRC for robust transportation control of a biglide parallel robot

Parallel robots, such as the Biglide mechanism, offer high stiffness and favorable dynamic performance, making them suitable for high-speed and high-precision applications. However, their closed-loop kinematic structure leads to strong nonlinear dynamics, coupling effects, and sensitivity to modeling uncertainties and external disturbances, which pose significant challenges for trajectory tracking control. To address these issues, this paper proposes a model-assisted extended state observer (MAESO)–based nonlinear active disturbance rejection control (ADRC) scheme for trajectory tracking control of a Biglide parallel robot. In the proposed approach, the nominal inertia matrix M ( q ) is incorporated into the observer-based compensation channel, allowing the lumped disturbance estimation and compensation process to better reflect the coupled MIMO dynamics of the Biglide robot. Furthermore, a nonlinear state error feedback control law is designed to better handle the inherent nonlinearities and coupling effects of the Biglide mechanism, thereby improving tracking performance and robustness. Compared with conventional ADRC and SMC, the proposed controller achieves improved tracking accuracy, faster transient response, and stronger robustness under nominal, disturbance-injected, and load-varying conditions. Comprehensive simulation and experimental studies are conducted to validate the effectiveness and robustness of the proposed method.