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 Reinforcement Q-learning PID controller for a restaurant mobile robot with double line-sensors
Tác giả hoặc Nhóm tác giả: Võ Như Thành, Đặng Phước Vinh, Lê Hoài Nam, Ngo Thanh Nghị, Đoàn Lê Anh
Nơi đăng: ACM International Conference Proceeding Series; Số: 978-145037631-0;Từ->đến trang: 164-167;Năm: 2020
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
The wheeled mobile robots have been widely applied in daily applications for its simplicity, robustness, stability and low-cost of manufacturing. Typically, PID controller is implemented to drive a mobile robot. However, the performance of the robot is greatly depended on the PID control parameters are tuned and the robustness of the mechanical system. Since the analog controllers were replaced by digital controller, PID control parameters could be automatically tuned to adapt the variation of the system parameters and operating condition. In this manuscript, a mathematical model is developed for a line following mobile robotic system which is used as a restaurant serving robot. The serving robot is controlled by an adaptive PID controller using Qlearning algorithm. The simulation and experimental results are compared to verify the advantage of the adaptive PID controller over classical PID controller.
ABSTRACT
The wheeled mobile robots have been widely applied in daily applications for its simplicity, robustness, stability and low-cost of manufacturing. Typically, PID controller is implemented to drive a mobile robot. However, the performance of the robot is greatly depended on the PID control parameters are tuned and the robustness of the mechanical system. Since the analog controllers were replaced by digital controller, PID control parameters could be automatically tuned to adapt the variation of the system parameters and operating condition. In this manuscript, a mathematical model is developed for a line following mobile robotic system which is used as a restaurant serving robot. The serving robot is controlled by an adaptive PID controller using Qlearning algorithm. The simulation and experimental results are compared to verify the advantage of the adaptive PID controller over classical PID controller.
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