An adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space

This paper presents an adaptive neural network approach to the trajectory tracking control of micro aerial vehicles especially when they are flying in a limited indoor area. Differing from conventional controllers, the proposed controller employs the outer position loop to directly generate angular velocity commands in the presence of unknown aerodynamics and disturbances and then the fast inner loop to handle the angular rate control. Adaptive neural networks are deployed to estimate all the uncertain factors with the adaptation law derived from the Lyapunov function. To achieve a real-time performance, a norm estimation approach of ideal weights is designed to achieve a high bandwidth and lighten the burden of computation burden. Meanwhile, a barrier Lyapunov function is introduced to guarantee the constraint of vehicle positions as well as the validity of the neural network estimation. Simulations and practical flight tests are conducted to verify the feasibility and effectiveness of the proposed control strategy.