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Immersion and Invariance-based Adaptive Control for Quadrotor Transportation Systems Using Deep Reinforcement Learning | IEEE Conference Publication | IEEE Xplore

Immersion and Invariance-based Adaptive Control for Quadrotor Transportation Systems Using Deep Reinforcement Learning


Abstract:

Quadrotor unmanned aerial vehicles (UAVs) are playing important roles in cargo transportation, especially in complex and unknown environments. However, the underactuated ...Show More

Abstract:

Quadrotor unmanned aerial vehicles (UAVs) are playing important roles in cargo transportation, especially in complex and unknown environments. However, the underactuated nature of the quadrotor UAV transportation systems makes it difficult for control schemes design. Considering system parameter uncertainties, a novel nonlinear adaptive control law is designed via Immersion and Invariance (I&I) method. Besides, in order to pursue faster convergence and stronger robustness, a novel control structure based on deep reinforcement learning is presented. The RL agent, which is designed through twin delayed deep deterministic policy gradient (TD3) algorithm, generates optimal control gains for the I&I adaptive controller in real-time. The stability of the closed-loop system are guaranteed through Lyapunov technique and LaSalle’s invariance theorem. Finally, by comparing with the classical control scheme, the superior performance and stronger robustness of the proposed controller are validated through numerous simulations.
Date of Conference: 09-11 July 2022
Date Added to IEEE Xplore: 29 November 2022
ISBN Information:
Conference Location: Guilin, China

Funding Agency:

Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China

I. Introduction

In the past decades, quadrotor UAVs have become popular due to their advantages of maneuverability [1] and the vertical take-off and landing (VTOL) capability [2], [3]. These features make the quadrotors ideal choices for cargo delivery by air [4]. Among the basic ways of the quadrotor transportation [5], cable-suspended load transportation has received increasing attention recently. However, the control of the quadrotor transportation systems is a much more challenging task compared with single quadrotor control problems due to the dual underactuated property, strong nonlinearity and complex dynamic coupling [6], hence it is of great necessity to design efficient control schemes for mission safety.

Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China

References

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