Adaptive Multi Surface Sliding Mode Control of a Quadrotor Slung Load System

In the utilization of multirotor UAVs for the transport of suspended payloads, a good level of accuracy and precision, with minimal vibration of the multirotor, and stability of the payload are desired characteristics. However, owing to the nonlinearity of the control design and the often underactuated nature of the UAV, designing a robust control for the system presents an interesting challenge. Furthermore, the system is subjected to both internal and external uncertainties and disturbances which may not always be mathematically defined. Hence, this research work proposes a multi-surface sliding mode controller, aided by RBF Neural Networks to approximate external disturbances that a multirotor UAV is subjected to. First, the dynamics of the multirotor with a suspended payload, represented by a single pendulum, will be presented. To ensure tracking stability, a multi-surface sliding mode controller based on an adaptive RBF neural network will then be proposed. Simulation results are presented to validate the effectiveness of the proposed controller.