This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles(AHVs).Considering that Reinforcement Learning(RL)has the advantage...This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles(AHVs).Considering that Reinforcement Learning(RL)has the advantage of exploring approximate optimal strategies,an RL-based assistance controller parallel to the fundamental controller is introduced to generate the assistance control signal.Specifically,the Incremental model-based Dual Heuristic Programming(IDHP)method is adopted to design the RL-based assistance control law.In order to extend the IDHP method to the assistance control scenario,a novel linear time-varying incremental model of the closed-loop augmented system is constructed and identified in real time,which consists of the AHV plant,the fundamental controller,and the command generator.The RL agent continuously updates its neural-network weights according to the real-time identification information,and adjusts its control policy,i.e.,the assistance control signal,after detecting sudden model changes.Simulation results have validated the effectiveness of the proposed intelligent fault-tolerant control scheme under various types of elevator faults and aerodynamic/configuration parameter uncertainties.The fault-tolerant ability of the whole control system with the proposed RL-based assistance controller is validated in both inner-loop attitude and outer-loop altitude tracking tasks.展开更多
This paper presents a robust finite-time visual servo control strategy for the tracking problem of omni-directional mobile manipulators(OMMs)subject to mismatched disturbances.First,the nonlinear kinematic model of vi...This paper presents a robust finite-time visual servo control strategy for the tracking problem of omni-directional mobile manipulators(OMMs)subject to mismatched disturbances.First,the nonlinear kinematic model of visual servoing for OMMs with mismatched disturbances is explicitly presented to solve the whole-body inverse kinematic problem.Second,a sliding mode observer augmented with an integral terminal sliding mode controller is proposed to handle these uncertainties and ensure that the system converges to a small region around the equilibrium point.The boundary layer technique is employed to mitigate the chattering phenomenon.Furthermore,a strict finite-time Lyapunov stability analysis is conducted.An experimental comparison between the proposed algorithm and a traditional position-based visual servo controller is carried out,and the results demonstrate the superiority of the proposed control algorithm.展开更多
基金co-supported by the Aeronautical Science Foundation of China(Nos.20220048051001,20230013051002)the“1912 Project”of China。
文摘This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles(AHVs).Considering that Reinforcement Learning(RL)has the advantage of exploring approximate optimal strategies,an RL-based assistance controller parallel to the fundamental controller is introduced to generate the assistance control signal.Specifically,the Incremental model-based Dual Heuristic Programming(IDHP)method is adopted to design the RL-based assistance control law.In order to extend the IDHP method to the assistance control scenario,a novel linear time-varying incremental model of the closed-loop augmented system is constructed and identified in real time,which consists of the AHV plant,the fundamental controller,and the command generator.The RL agent continuously updates its neural-network weights according to the real-time identification information,and adjusts its control policy,i.e.,the assistance control signal,after detecting sudden model changes.Simulation results have validated the effectiveness of the proposed intelligent fault-tolerant control scheme under various types of elevator faults and aerodynamic/configuration parameter uncertainties.The fault-tolerant ability of the whole control system with the proposed RL-based assistance controller is validated in both inner-loop attitude and outer-loop altitude tracking tasks.
基金supported by the Artificial Intelligence Innovation and Development Special Fund of Shanghai(No.2019RGZN01041)the National Natural Science Foundation of China(No.92048205).
文摘This paper presents a robust finite-time visual servo control strategy for the tracking problem of omni-directional mobile manipulators(OMMs)subject to mismatched disturbances.First,the nonlinear kinematic model of visual servoing for OMMs with mismatched disturbances is explicitly presented to solve the whole-body inverse kinematic problem.Second,a sliding mode observer augmented with an integral terminal sliding mode controller is proposed to handle these uncertainties and ensure that the system converges to a small region around the equilibrium point.The boundary layer technique is employed to mitigate the chattering phenomenon.Furthermore,a strict finite-time Lyapunov stability analysis is conducted.An experimental comparison between the proposed algorithm and a traditional position-based visual servo controller is carried out,and the results demonstrate the superiority of the proposed control algorithm.
