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2020, Volume 21, Issue 5

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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 5 doi: 10.1631/FITEE.1900641

Proximal policy optimization with an integral compensator for quadrotor control

东南大学自动化学院，中国南京市，210096

Received: 2019-11-22 Accepted: 2020-05-18 Available online: 2020-05-18
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Abstract

We use the advanced (PPO) algorithm to optimize the stochastic control strategy to achieve speed control of the “model-free” quadrotor. The model is controlled by four learned s, which directly map the system states to control commands in an end-to-end style. By introducing an integral compensator into the actor-critic framework, the speed tracking accuracy and robustness have been greatly enhanced. In addition, a two-phase learning scheme which includes both offline- and online-learning is developed for practical use. A model with strong generalization ability is learned in the offline phase. Then, the flight policy of the model is continuously optimized in the online learning phase. Finally, the performances of our proposed algorithm are compared with those of the traditional PID algorithm.

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