H∞ Control of Nonaffine Aerial Systems Using Off-policy Reinforcement Learning

This paper presents a completely model-free H∞ optimal tracking solution to the control of a general class of nonlinear nonaffine systems in the presence of the input constraints. The proposed method is motivated by nonaffine unmanned aerial vehicle (UAV) system as a real application. First, a general class of nonlinear nonaffine system dynamics is presented as an affine system in terms of a nonlinear function of the control input. It is shown that the optimal control of nonaffine systems may not have an admissible solution if the utility function is not defined properly. Moreover, the boundness of the optimal control input cannot be guaranteed for standard performance functions. A new performance function is defined and used in the L2-gain condition for this class of nonaffine system. This performance function guarantees the existence of an admissible solution (if any exists) and boundness of the control input solution. An off-policy reinforcement learning (RL) is employed to iteratively solve the H∞ optimal tracking control online using the measured data along the system trajectories. The proposed off-policy RL does not require any knowledge of the system dynamics. Moreover, the disturbance input does not need to be adjustable in a specific manner.