Abstract
The helicopter Trailing-Edge Flaps(TEFs)technology is one of the recent hot topics in morphing wing research.By employing controlled deflection,TEFs can effectively reduce the vibra-tion level of helicopters.Thus,designing specific vibration reduction control methods for the heli-copters equipped with trailing-edge flaps is of significant practical value.This paper studies the optimal control problem for helicopter-vibration systems with TEFs under the framework of adap-tive dynamic programming combined with Reinforcement Learning(RL).Time-delay and distur-bances,caused by complexity of helicopter dynamics,inevitably deteriorate the control performance of vibration reduction.To solve this problem,a zero-sum game formulation with a linear quadratic form for reducing vibration of helicopter systems is presented with a virtual predic-tor.In this context,an off-policy reinforcement learning algorithm is developed to determine the optimal control policy.The algorithm utilizes only vertical vibration load data to achieve a policy that reduces vibration,attains Nash equilibrium,and addresses disturbances while compensating for time-delay without knowledge of the dynamics of the helicopter system.The effectiveness of the proposed method is demonstrated in a virtual platform.
基金项目
National Natural Science Foundation of China(62022060)
National Natural Science Foundation of China(62073234)
National Natural Science Foundation of China(62073158)
National Natural Science Foundation of China(62373268)
National Natural Science Foundation of China(62373273)
Basic Research Project of Education Department of Liaoning Province,China(LJKZ0401)
NETL
NSTL
万方数据