Hybrid neural network under-actuated sliding-mode control for trajectory tracking of quad-rotor unmanned aerial vehicle

Due to the under-actuated characteristic of quadrotor unmanned aerial vehicle (QUAV), the sliding surface using measurable output (i.e., 3D position and attitude), whose number is larger than that of control input (i.e., total thrust force, roll, pitch and yaw torques), is designed. Hence, the number of control input and sliding surface is the same, and the indirectly controlled mode (e.g., x- and y-axes) is controlled. Under uncertain environment, the sliding-mode under-actuated control (SMUC) with suitable conditions is first derived so that asymptotical and bounded tracking results are achieved. To improve system performance, an on-line recurrent neural network modeling for dynamical uncertainty of QUAV is employed to design a recurrent-neural-network-based sliding-mode under-actuated control (RNNSMUC). Then the proposed hybrid neural-network-based sliding-mode under-actuated control (HNNSMUC) combining SMUC and RNNSMUC with a transition maintains both advantages of SMUC and RNNSMUC and simultaneously avoids the disadvantages coming from SMCU and RNNSMUC.