Learning based control compensation for multi-axis gimbal systems using inverse and forward dynamics
Embargo Lift Date: 2022-04-06
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Unmanned aerospace vehicles (such as rockets, drones, and satellites) usually carry sensors as their primary payload. These sensors (i.e., electro-optical and/or infrared imaging cameras) are used for image processing, target tracking, surveillance, mapping, and providing high-resolution imagery for environmental surveys. It is crucial to obtain a steady image in all of those applications. This is typically accomplished by using multi-axis gimbal systems. This study concentrates on the modeling and control of a multi-axis gimbal system that will be mounted on a surface-to-surface tactical missile. A novel and fully detailed procedure is proposed to derive the nonlinear and highly coupled EOMs (Equations of Motion) of the two-axis gimbal system. Different from the existing works, Forward Dynamics of the two-axis gimbal system is modeled using multi-body dynamics modeling techniques. In addition to Forward Dynamics model, Inverse Dynamics model is generated to estimate the complementary torques associated with the state and mechanism-dependent, complex disturbances acting on the system. Forward and Inverse Dynamics models are used in Monte Carlo Simulations (MCSs) for Sensitivity Analysis. A multilayer perceptron (MLP) structure based disturbance compensator is implemented to cope with external and internal disturbances and parameter uncertainities through torque input channel. Comparisons with well known controllers such as cascaded PID, ADRC (Active Disturbance Rejection Control), Inverse Dynamics based controllers show that the NN (neural network)-based controller is more succesful in the full operational range without requiring any tuning or adjustment. Implementation of MLP assisted closed-loop control with simulations using Simulink® are performed. Finally, proposed control algorithms are tested on the physical system by using Simulink® Real-Time (xPC Target). Comparative results are presented in figures and tables in the thesis.
KeywordsMmulti-axis gimbal system
Multi-body dynamics modeling
Disturbance torque compensation
Monte Carlo simulations