Learning Time Optimal Control of Smart Actuators with Unknown Friction

Active valves are most effective tools to control gas flow in compressors if fast transitions between the open mode and closed mode are needed. Unfortunately, an accurate model including several nonlinear effects and in particular the resistance and gas flow forces is not available, and this prevents the use of standard model based approaches for time optimal control. However, the repetitive nature of the operation of valves suggests the use of learning methods to track a reference in spite of the insufficient information on the control behavior, thus shifting the problem from the search of the time optimal control to the search of the reference corresponding to its solution. To this end, in this paper, a previously proposed algorithm for the iterative determination of the fastest feasible trajectory is analyzed in terms of convergence conditions and applied to the valve model.