Trajectory optimization and stabilization in the control of pneumatically actuated drones

Presenter's Name(s)

Gabe Johnson

Abstract

As humans prepare for lunar and deep-space missions, autonomous systems for atmosphere- lacking environments are essential. This paper advances control methodologies for a pneumatically actuated 2D hopper. We develop and compare delayed hysteresis, PID with anti- windup, bang-bang control, MPC, and reinforcement learning (DDPG) combined with Kalman filtering for state estimation. Using LiDAR and MPU6050 sensor data, the system improves responsiveness through accelerometer feedback. Experimental results show model-informed observation strategies outperform traditional methods, offering a robust platform for testing autonomous control in conditions analogous to vacuum environments.

Primary Faculty Mentor Name

Jeffrey Marshall

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Mechanical Engineering

Primary Research Category

Engineering and Math Science

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Trajectory optimization and stabilization in the control of pneumatically actuated drones

As humans prepare for lunar and deep-space missions, autonomous systems for atmosphere- lacking environments are essential. This paper advances control methodologies for a pneumatically actuated 2D hopper. We develop and compare delayed hysteresis, PID with anti- windup, bang-bang control, MPC, and reinforcement learning (DDPG) combined with Kalman filtering for state estimation. Using LiDAR and MPU6050 sensor data, the system improves responsiveness through accelerometer feedback. Experimental results show model-informed observation strategies outperform traditional methods, offering a robust platform for testing autonomous control in conditions analogous to vacuum environments.