UAH trajectory tracking control based on RBFNN and improved RISE control techniques

DOI:	10.11809/bqzbgcxb2023.07.029
Keywords:	unmanned aerial helicopter; trajectory tracking control; compound disturbance; neural network; Robust Integral of Signum Error (RISE)
Abstract:	By considering the influence of model uncertainty and external disturbances, based on Radial Basis Function Neural Network (RBFNN) and improved Robust Integral of Signum Error (RISE) techniques, this paper establishes a design method for trajectory tracking control of an unmanned aerial helicopter (UAH). Firstly, a UAH nonlinear system model with uncertainty and external disturbances is established. Then, the tracking error is utilized as the input signal of the RBFNN to approximate the compound disturbance composed of uncertainty and external disturbances. Secondly, the weight combination of the filtering signal and its change rate is used as the input signal of RISE to design the controller so as to reduce the degree of the dependence of the proposed control design scheme on the UAH dynamic model. Thirdly, with the help of Lyapunov stability theory, the stability of the integrated closed loop tracking error system is analyzed, and the selection method of the control parameters is presented. Finally, with the aid of the UAH system model in the existing literature, the simulation and comparing results verify the effectiveness and superiority of the proposed control algorithm.
Published:	2023-07-28
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