Deep Reinforcement Learning Based Online Parameter Tuning for Active Disturbance Rejection Control and Application in Ship Course Tracking
Author:
Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-6328-4_6
Reference15 articles.
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2. Hu, J., Guo, J., Ding, B.: Automatic control of paving ship based on LSTM. In: 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, China, pp. 527-530 (2019)
3. Hu, J., Guo, J., Ding, B.: Automatic docking for underactuated ships based on multi-objective nonlinear model predictive control. IEEE Access 8, 70044–70057 (2020)
4. Du, J., Guo, C., Yu, S., Zhao, Y.: Adaptive autopilot design of time-varying uncertain ships with completely unknown control coefficient. IEEE J. Oceanic Eng. 32(2), 346–352 (2007)
5. Johansen, T., Perez, T., CristofaroLiu, A.: Ship collision avoidance and colregs compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Trans. Intell. Transp. Syst. 17(12), 3407–3422 (2016)
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