Reinforcement-Learning-based Magneto-hydrodynamic Control of Hypersonic Flows

In this work, we design a policy-iteration-based Q-learning approach for on-line optimal control of ionized hypersonic flow at the inlet of a scramjet engine. Magneto-hydrodynamics (MHD) has been recently proposed as a means for flow control in various aerospace problems. This mechanism corresponds to applying external magnetic fields to ionized flows towards achieving desired flow behavior. The applications range from external flow control for producing forces and moments on the air-vehicle to internal flow control designs, which compress and extract electrical energy from the flow. The current work looks at the later problem of internal flow control. The baseline controller and Q-function parameterizations are derived from an off-line mixed predictive-control and dynamic-programming-based design. The nominal optimal neural network Q-function and controller are updated on-line to handle modeling errors in the off-line design. The on-line implementation investigates key concerns regarding the conservativeness of the update methods. Value-iteration-based update methods have been shown to converge in a probabilistic sense. However, simulations results illustrate that realistic implementations of these methods face significant training difficulties, often failing in learning the optimal controller on-line. The present approach, therefore, uses a policy-iteration-based update, which has time-based convergence guarantees. Given the special finite-horizon nature of the problem, three novel on-line update algorithms are proposed. These algorithms incorporate different mix of concepts, which include bootstrapping, and forward and backward dynamic programming update rules. Simulation results illustrate success of the proposed update algorithms in re-optimizing the performance of the MHD generator during system operation