Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
Abstract: The paper presents a technique using reinforcement learning (RL) to adapt the control gains of a quadcopter controller. Specifically, we employed Proximal Policy Optimization (PPO) to train a policy which adapts the gains of a cascaded feedback controller in-flight. The primary goal of this controller is to minimize tracking error while following a specified trajectory. The paper's key objective is to analyze the effectiveness of the adaptive gain policy and compare it to the performance of a static gain control algorithm, where the Integral Squared Error and Integral Time Squared Error are used as metrics. The results show that the adaptive gain scheme achieves over 40$\%$ decrease in tracking error as compared to the static gain controller.
- Hsiao, F.-I., and Chiang, C.-M., “Reinforcement Learning Based Quadcopter Controller,” Fall 2019.
- Xue-song Wang, W. S., Yuhu Cheng, “A Proposal of Adaptive PID Controller Based on Reinforcement Learning,” Journal of China University of Mining and Technology, 2007. http://dx.doi.org/10.1016/S1006-1266(07)60009-1.
- Arzaghi, H., “Adaptive PID Controller Based on Reinforcement Learning fro Wind Turbine Control,” 2008.
- T. Shuprajhaa, K. S., Shiva Kanth Sujit, “Reinforcement learning based adaptive PID controller design for control of linear/nonlinear unstable processes,” Applied Soft Computing, 2022. https://doi.org/10.1016/j.asoc.2022.109450.
- Shipman, W. J., and Coetzee, L. C., “Reinforcement Learning and Deep Neural Networks for PI Controller Tuning,” 2019. https://doi.org/10.1016/j.ifacol.2019.09.173.
- URL https://doi.org/10.1007/978-90-481-9707-1.
- Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O., “Proximal Policy Optimization Algorithms,” 2017.
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