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 vibration level of helicopters.Thus,desig...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 vibration level of helicopters.Thus,designing specific vibration reduction control methods for the helicopters 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 adaptive dynamic programming combined with Reinforcement Learning(RL).Time-delay and disturbances,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 predictor.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.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.62022060,62073234,62073158,62373268,62373273)the Basic Research Project of Education Department of Liaoning Province,China(No.LJKZ0401).
文摘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 vibration level of helicopters.Thus,designing specific vibration reduction control methods for the helicopters 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 adaptive dynamic programming combined with Reinforcement Learning(RL).Time-delay and disturbances,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 predictor.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.
