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Plasma start-up is typically achieved manipulating poloidal magnetic fields, gas injection and possibly auxiliary heating. Model-based design techniques have been gaining increasing attention in view of future large tokamaks which have more stringent constraints and less room for trial-and-error. In this paper, we formulate the tokamak start-up scenario design problem as a constrained optimization problem and introduce a novel shot-to-shot correction algorithm, based on the Iterative Learning Control concept, to compensate for unavoidable modeling errors based on experimental data. The effectiveness of the approach is demonstrated in experiments on the TCV tokamak showing that the target ramp-up scenario could be obtained in a small number of shots with a rough electromagnetic model.

This paper describes the control architecture and the control laws of a new concept of Modular Iron Bird aimed at reproducing flight loads to test mobile aerodynamic control surface actuators for small and medium size aircraft and Unmanned Aerial Vehicles. The iron bird control system must guarantee the actuation of counteracting forces. On one side, a hydraulic actuator simulates the hinge moments acting on the mobile surface due to aerodynamic and inertial effects during flight; on the other side, the actuator to be tested applies an active hinge moment to control the angular position of the same surface. Reference aerodynamic and inertial loads are generated by a flight simulation module to reproduce more realistic conditions arising during operations. The design of the control action is based on a dynamic model of the hydraulic plant used to generate loads. This system is controlled using a Proportional Integral Derivative control algorithm tuned with an optimization algorithm taking into account the closed loop dynamics of the actuator under testing, uncertainties and disturbances in the controlled plant. Numerical simulations are presented to show the effectiveness of the proposed architecture and control laws.