Surrogate‐Based Robust Tracking Controller for a Lambda Robot

Tracking control of multibody systems typically requires precise inversion of complex kinematic and dynamic equations, which can be particularly challenging for closed‐loop mechanisms. This study presents a surrogate‐based control approach that leverages artificial neural networks to approximate inverse kinematics and dynamics using data generated from classical simulations of a nominal model. These data‐driven surrogates enable feedforward prediction of joint trajectories and control inputs without requiring analytical model inversion. To ensure robustness under modeling errors and parametric uncertainties, additional inner‐ and outer‐loop sliding mode control (SMC) loops are integrated. Simulation results from a lambda robot demonstrate high tracking accuracy with asymptotic steady‐state errors even under perturbations of system parameters. The proposed framework establishes a structured approach for trajectory tracking by combining data‐driven feedforward and robust feedback control suitable for complex nonlinear robotic systems.