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Tracking control of multibody systems typically requires a deep understanding of system kinematics and dynamics. For closed-loop mechanisms, however, this becomes challenging, which is why this study introduces a novel method that utilizes artificial intelligence (AI) to simplify the process, making it possible to achieve effective trajectory control with minimal technical expertise [1].
A key component is to use surrogates for inverse models based on artificial neural networks, which will be purely trained from data being generated, e.g., by classical simulation of a nominal model of the lambda robot. With these nominal surrogates for inverse kinematics and inverse dynamics, a well-performing linear quadratic regulator (LQR) controller based on linear feedback approximation is designed to track the desired trajectory.
This works perfectly well if the plant is consistent with the nominal model used for data generation. The behavior of a real model with disturbed parameters, however, may deviate from an ideal tracking. Such model uncertainties, such as disturbance and external forces, can be taken into account by an additional feedback loop to adjust the control signal w.r.t discrepancies between the real tracking path of the system and the desired track.
Different parametric uncertainties are considered to examine the robustness of the proposed concept. First results indicate excellent tracking performance within an adequate range of error, and validate the efficacy of this surrogate-based control approach, highlighting its adaptability and reliability for complex robotic systems without insight in details of their equations of motion.
[1] Hajipour, S.; Bestle, D. (2024) “Data-based Design of a Tracking Controller for Planar Closed-loop Mechanisms”. In: Proc. of NAFEMS Nordic Conference on AI and ML in Simulation Driven Design, Lund.
