针对现有无人直升机主动容错控制方法未考虑故障估计误差对编队控制精度影响的问题,建立故障估计误差与辅助控制器的博弈关系,将零和微分博弈理论与全驱系统方法相结合,融合设计出一种分层容错编队控制策略。首先,引入虚拟控制变量,并建立考虑执行器故障的无人直升机高阶全驱系统模型,包括位置外环和姿态内环高阶全驱子系统。然后,设计自适应故障估计器对无人直升机执行器故障进行有效估计。接着,结合故障估计信息和全驱系统方法,针对无人直升机内外环分层设计位置和姿态容错控制器,确保系统状态在故障下仍能实现编队控制目标。此外,引入辅助控制器与故障估计误差参与的零和微分博弈模型,通过动态事件触发机制下的自适应动态规划算法来获取近似最优解,以最小代价有效补偿故障估计误差对编队性能的影响。最后,通过仿真实验说明所提出控制方案的有效性和优越性。
Aiming at the problem that the existing active fault-tolerant control methods for unmanned helicopters do not consider the influence of fault estimation errors on the formation control accuracy, the game relationship between fault estimation errors and auxiliary controllers is established. By integrating zero-sum differential game theory and the fullactuated system approach, a hierarchical fault-tolerant formation control strategy is developed. Firstly, virtual control variables are introduced, and the high-order fully actuated system model of the unmanned helicopter with actuator fault is established, including the position outer-loop and attitude inner-loop high-order fully actuated subsystems. Subsequently, an adaptive fault estimator is designed to estimate the fault of unmanned helicopter effectively. Then, combining the fault estimation information and the fully actuated system approach, the position and attitude fault-tolerant controllers are designed in a hierarchical manner for the inner and outer loops of the unmanned helicopter, which can ensure that the system state satisfies the formation control objective under faults. In addition, a zero-sum differential game model between auxiliary controller and fault estimation error is introduced, and the approximate optimal solution is obtained by adaptive dynamic programming algorithm under the dynamic event-triggered mechanism, effectively compensating for the impact of the fault estimation error on the formation performance at minimum cost. Finally, the effectiveness and superiority of the proposed control scheme are demonstrated by simulation experiments.
[1]吴希明, 牟晓伟.直升机关键技术及未来发展与设想[J].空气动力学学报, 2021, 39(3):1-10
[2]邓景辉.高速直升机关键技术与发展[J].航空学报, 2024, 45(9):529085-
[3]Feng Y, Zhou Y, Ho H W.Reinforcement learning based robust tracking control for unmanned helicopter with state constraints and input saturation[J].Aerospace Science and Technology, 2024, 155:109549-
[4]Kuo C W, Tsai C C, Lee C T.Intelligent leader-following consensus formation control using recurrent neural networks for small-size unmanned helicopters[J].IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(2):1288-1301
[5]Wang D, Zong Q, Tian B, et al.Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters[J].ISA transactions, 2018, 73:208-226
[6]Li J, Zhang G, Zhang X, et al.Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UAVs cooperative formation system for maritime parallel search[J].IEEE Transactions on Intelligent Transportation Systems, 2024, 25(5):3986-3998
[7]Yu Z, Li J, Xu Y, et al.Reinforcement learning-based fractional-order adaptive fault-tolerant formation control of networked fixed-wing UAVs with prescribed performance[J].IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3):3365-3379
[8]Sun J, Xu Z, Zhang H, et al.Adaptive distributed control of nonlinear multiagent systems with event-triggered for communication faults and dead-zone inputs[J].IEEE Transactions on Cybernetics, 2024, 54(10):5877-5886
[9]张晓龙, 李荣, 阎高伟, 等.小型无人直升机故障估计与容错控制[J].航空学报, 2024, 45(S1):730802-
[10]Wang X, Tan C P.Output feedback active fault tolerant control for a 3-DOF laboratory helicopter with sensor fault[J].IEEE Transactions on Automation Science and Engineering, 2024, 21(3):2689-2700
[11]Chen M, Yan K, Wu Q.Multiapproximator-based faulttolerant tracking control for unmanned autonomous helicopter with input saturation[J].IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(9):5710-5722
[12]朱骏杰, 张柯, 姜斌, 等.基于宽度神经网络的直升 机预设时间容错控制[J].控制与决策, 2025, :-
[13]Yu Y, Guo J, Chadli M, et al.Distributed adaptive fuzzy formation control of uncertain multiple unmanned aerial vehicles with actuator faults and switching topologies[J].IEEE Transactions on Fuzzy Systems, 2023, 31(3):919-929
[14]Qian M, Zhang Z, Zheng Z, et al.Sliding mode control-based distributed fault tolerant tracking control for multiple unmanned aerial vehicles with input constraints and actuator faults[J].International Journal of Robust and Nonlinear Control, 2023, 33(15):9150-9173
[15]Yang H, Jiang B, Liu H H T, et al.Attitude synchronization for multiple 3-DOF helicopters with actuator faults[J].IEEEASME Transactions on Mechatronics, 2019, 24(2):597-608
[16]Liu C, Jiang B, Zhang K, et al.Hierarchical structurebased fault-tolerant tracking control of multiple 3-DOF laboratory helicopters[J].IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(7):4247-4258
[17]Miao Q, Zhang K, Jiang B.Incremental fully actuated system approach-based prescribed-time fault tolerant formation control of helicopters under multiple faults[J].Aerospace Science and Technology, 2024, 151:109334-
[18]段广仁.高阶系统方法—全驱系统与参数化设计[J].自动化学报, 2020, 46(07):1333-1345
[19]段广仁.高阶系统方法—Ⅱ能控性与全驱性[J].自动化学报, 2020, 46(08):1571-1581
[20]段广仁.高阶系统方法—Ⅲ能观性与观测器设计[J].自动化学报, 2020, 46(09):1885-1895
[21]Cui K X, Duan G R, Hou M Z.Discrete-time model reference tracking control for a class of combined spacecraft: A high-order fully actuated system approach[J].IEEE Transactions on Automation Science and Engineering, 2024, 21(4):6966-6977
[22]Wang X, Duan G R.Comprehensive reconstructions and predictive control for quadrotor UAV information gathering tracking missions based on fully actuated system approaches[J].ISA transactions, 2024, 147:540-553
[23]Lan J, Liu Y J, Yu D, et al.Time-varying optimal formation control for second-order multiagent systems based on neural network observer and reinforcement learning[J].IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3):3144-3155
[24]Zhang B, Lv M, Cui S, et al.Learning-based optimal cooperative formation tracking control for multiple UAVs: A feedforward-feedback design framework[J].IEEE Transactions on Automation Science and Engineering, 2025, 22:11-23