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In this work, we provide a novel optimal guidance and control strategy for lunar hopper obstacle-avoidance, descent, and landing problem and demonstrate its behavior using numerical simulations. More specifically, the major contributions of this paper are three-fold: 1) proposed a feedback-based reference trajectory design for lunar hopper guidance, 2) developed the mathematical models and equations of linear quadratic tracking (LQT) controller for lunar hopper control, and 3) developed a method using reinforcement learning to optimize the designed reference trajectory in conjunction with the designed LQT controller, the so- called linear quadratic tracking with reinforcement learning based reference trajectory optimization (LQT-RTO). We demonstrated the LQT-RTO under a 2-dimensional (2D) lunar hopper simulation environment with 1) the LQT with heuristic reference trajectory design (LQT-HTD) and 2) reinforcement learning (reinforcement learning based controller, or RLC). We confirmed by numerical simulation that the LTQ-RTO outperformed the LQT-HTD in terms of fuel consumption, and outperformed the RLC in terms of landing success rate. Lastly, we provided theoretical interpretation to the simulation results.

Linear Quadratic Tracking With Reinforcement Learning Based Reference Trajectory Optimization for the Lunar Hopper in Simulated Environment