Non-linear Intelligent Control Design for Unconventional Unmanned Aerial Vehicle

View Video Presentation: https://doi.org/10.2514/6.2023-1071.vid Design complexities of trending UAVs and the operational harsh environments necessitates Control Law formulation utilizing intelligent techniques that are both robust, model-free and adaptable. In this research, an intelligent control architecture for an Unmanned Aerial Vehicle (UAV) having an unconventional inverted V-tail design, is presented. Due to unique design of the vehicle strong roll and yaw coupling exists, making the control of vehicle challenging. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. This stabilization and tracking controller for the UAV achieves the goal of an optimal flight path and exhibits satisfactory performance. The paper illustrates the application of modified DDPG algorithm towards the design, while the performance of the resulting controller is assessed in simulation using dynamic model of the vehicle. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory and another DRL method named Proximal Policy Optimization (PPO). Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significance utility to airborne applications.