Run-to-run control with Bayesian optimization for soft landing of short-stroke reluctance actuators

There is great interest in minimizing the impact forces of reluctance actuators during commutations, in order to reduce contact bouncing, acoustic noise, and mechanical wear. In this regard, a run-to-run (R2R) control algorithm is proposed to decrease the contact velocity, by exploiting the repetitive operations of these devices. The complete control is presented, with special focus on the optimization method and the input definition. The search method is based on Bayesian optimization, and several additions are introduced for its application in R2R control, e.g., the removal of stored points and the definition of a new acquisition function. Additionally, methods for the input parameterization and dimension reduction are presented. For analysis, in this article, Monte Carlo simulations are performed using a dynamic model of a commercial solenoid valve, comparing the proposed search method with two alternatives. Furthermore, the control strategy is validated through experimental testing, using several devices from the same ensemble of solenoid valves.