Online sensorless rapid thrust control of a motor-propeller system through offline data-driven modeling

Motor-propeller systems are widely used in Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for thrust generation and motion control. However, many practical platforms do not equip force sensors or encoders because of additional cost, weight, and integration complexity, making rapid thrust tracking without sensing a challenging problem. Existing open-loop thrust control strategies commonly assume an instantaneous thrust response and therefore suffer from significant transient errors and response delays when thrust commands change rapidly or when the propulsion system has large inertia. To address this issue, this paper proposes a data-driven sensorless rapid thrust control strategy for motor-propeller systems. The proposed approach establishes a neural-network-based data-driven model that predicts the thrust at the next step from previous control inputs and estimated thrust states without requiring online sensing. The learned model is integrated with model predictive control (MPC) and a mode-switching mechanism to dynamically determine aggressive control inputs that improve thrust responsiveness while maintaining stability. Experiments are conducted on a real BLDC motor-propeller system and compared with conventional open-loop control and sensor-based PD, MPC, and sliding mode control (SMC) strategies. Results show that the proposed strategy significantly improves transient performance over the widely used open-loop strategy. For tracking a 0.5 Hz square-wave thrust trajectory, the proposed method reduces rise time from 0.141 s to 0.037 s and fall time from 0.122 s to 0.058 s, while also reducing the mean absolute thrust tracking error from 0.314 N to 0.271 N. For randomly varying thrust commands updated every 0.4 s, the proposed method achieves the best overall performance, reducing rise time by 86.0% and fall time by 60.6% compared with the open-loop strategy. Moreover, despite operating without online sensors, the proposed strategy achieves thrust tracking performance comparable to sensor-based PD, MPC, and SMC controllers.