A variational and symplectic framework for model-free control: preliminary results
For control engineers, this offers a potential robustness improvement over existing model-free control, but the work is preliminary and incremental.
The authors propose a variational and symplectic extension of model-free control to improve robustness, achieving auto-tuning of the key parameter and enhanced tracking stability. No concrete numerical results are provided.
The model-free control approach is an advanced control law that requires few information about the process to control. Since its introduction in 2008, numerous applications have been successfully considered, highlighting attractive robustness properties towards tracking efficiency and disturbance rejection. In this work, a variational approach of the model-free control is proposed in order to extend its robustness capabilities. An adaptive formulation of the controller is proposed using the calculus of variations within a symplectic framework, that aims to consider the control law as an optimization problem toward the auto-tuning of its main key parameter. The proposed formulation provides a coupling between the model-free control law and a variational integrator to improve the robustness of the tracking towards process changes and emphasize closed-loop stabilization. Some illustrative examples are discussed to highlight the rightness of the proposed approach.