Neural Network Sliding Control for Three-Axis Gimbal Orientation with Camera on an Unmanned Vehicle

The article focuses on development and modeling of neural network sliding mode (NSM) algorithms for controlling three-axis gimbal (TAG) orientation with camera of an unmanned aerial vehicle (UAV). The NSM algorithm is based on kinematic equations that describe the rotation and interaction of three rigid TAG components: yaw channel frame (YCF), roll channel frame (RCF), and pitch channel frame (PCF). The mathematical model of the TAG takes into account the interaction of three rigid components (YCF, RCF and PCF) are the TAG components, and the influence of unknown disturbances on the TAG. Disturbances (centrifugal force, gravity, friction that arise during TAG working) significantly complicate the mathematical model of the TAG. The problems are solved by synthesizing an adaptive sliding mode control (ASMC) using an artificial neural network (ANN) RBF. In RBF ANN, radial basis functions (Gaussoids) serve as nonlinear activation functions. In the description of the sliding control mode, disturbances are introduced that contain unknown parameters: influence of gravity, influence of the centrifugal force of inertia of the frames, etc. Unknown parameters of disturbances in NSM are estimated by using RBF ANN. The combination of sliding mode control and RBF neural network implements neural network sliding control for the TAG orientation. The modeling results of neural network sliding mode control in MATLAB prove that system using NSM has a large stability margin, is characterized by a higher quality of control processes and working quite stably in unknown and random disturbances environment compared to the classic sliding mode controller.

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