This paper considers the problem on tracking control for the medium-scale unmanned autonomous helicopter (UAH) system under external disturbances, actuator faults, and full state constraints. In comparison with existing results, this paper does not only consider asymmetric state constraints but also jointly take the faults and disturbances into account on the UAH system, estimating and compensating for them separately, which can effectively enhance the tracking performance. First, the dynamics of nonlinear UAH system is divided into the position subsystem and the attitude one. Second, an improved barrier Lyapunov function (BLF) is constructed to handle the problem of asymmetric state constraints. Third, in order to estimate the external disturbances and actuator faults, two coupled generalized proportional integral observers (GPIOs) and two fault estimators are designed, respectively. Fourth, based on above estimations, improved BLFs, and backstepping method, two tracking control laws for the position and attitude loops are presented and an overall closed-loop system is established. Then, a sufficient condition ensuring uniform boundedness of tracking and estimation errors is derived. Finally, some simulating results demonstrate the effectiveness and advantage of the proposed control scheme.
ChenMShiPLimCC (2016a) Adaptive neural fault-tolerant control of a 3-DOF model helicopter system. IEEE Transactions on Systems, Man, and Cybernetics: Systems46(2): 260–270.
2.
ChenMTaoGJiangB (2015) Dynamic surface control using neural networks for a class of uncertain nonlinear systems with input saturation. IEEE Transactions on Neural Networks and Learning Systems26(9): 2086–2097.
3.
ChenWH (2004) Disturbance observer based control for nonlinear systems. IEEE/ASME Transactions on Mechatronics9(4): 706–710.
4.
ChenWHYangJGuoL, et al. (2016b) Disturbance observer-based control and related methods-an overview. IEEE Transactions on Industrial Electronics63(2): 1083–1095.
5.
GaoTLiuYLiuL (2018) Adaptive neural network-based control for a class of nonlinear purefeedback systems with time-varying full state constraints. IEEE/CAA Journal of Automatica Sinica5(5): 923–933.
6.
GaoZDingX (2007) Actuator fault robust estimation and fault-tolerant control for a class of nonlinear state-space systems via descriptor system approach. Automatic43: 912–920.
7.
GongQTLiuZPHuX, et al. (2023) Robust anti-disturbance fault-tolerant control of ship-board platforms with multiplicative actuator faults and unknown disturbances. Ocean Engineering286(2): 115552.
8.
HeWMaTZ (2013) Adaptive neural network control of a vessel with output constraints using the asymmetric barrier Lyapunov function. IFAC Proceedings Volumes46(20): 246–251.
9.
KuoCTsaiCLeeC (2021) Intelligent leader-following consensus formation control using recurrent neural networks for small-size unmanned helicopters. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(2): 1288–1301.
10.
LaiYLeT (2021) Adaptive learning-based observer with dynamic inversion for the autonomous flight of an unmanned helicopter. IEEE Transactions on Aerospace and Electronic Systems57(3): 1803–1814.
11.
LiMZuoZYLiuH, et al. (2018) Adaptive fault tolerant control for trajectory tracking of a quadrotor helicopter. Transactions of the Institute of Measurement and Control40(12): 3560–3569.
12.
LiangXLYaoZKGeYW, et al. (2023) Disturbance observer based actor-critic learning control for uncertain nonlinear systems. Chinese Journal of Aeronautics36(11): 271–280.
13.
LiuCLuKSunJ, et al. (2019) Fault tolerant control with disturbance estimator for a class of linear parameter varying systems subject to time-varying fault. Transactions of the Institute of Measurement and Control41(13): 3836–3846.
14.
LiuCJChenWAndrewsJ (2012) Tracking control of small-scale helicopters using explicit nonlinear MPC augmented with disturbance observers. Control Engineering Practice20(3): 258–268.
15.
LiuHWangXZhongY (2014) Robust position control of a lab helicopter under wind disturbances. IET Control Theory and Applications8(15): 1555–1565.
16.
LiuLGaoTTLiuYJ, et al. (2021) Time-varying IBLFs-based adaptive control of uncertain nonlinear systems with full state constraints. Automatica129(5): 109595.
17.
LvMYuWBaldiS (2021) The set-invariance paradigm in fuzzy adaptive DSC design of large-scale nonlinear input-constrained systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(2): 1035–1045.
18.
MarconiLNaldiR (2007) Robust full degree-of-freedom tracking control of a helicopter. Automatica43(11): 1909–1920.
19.
MettlerB (2002) System identification modeling of a small-scale unmanned rotorcraft for flight control design. Journal of the American Helicopter Society47(1): 50–63.
20.
MokhtariMCherkiBBrahamA (2017) Disturbance observer based hierarchical control of coaxial-rotor UAV disturbance observer based hierarchical control of coaxial-rotor UAV. ISA Transactions67: 466–475.
21.
ShenFWangXYinX (2020) BLF-based adaptive DSC for a class of stochastic nonlinear systems of full state constraints with time delay and hysteresis input. Neurocomputing386: 244–256.
22.
WanMChenMYongKN (2022) Adaptive tracking control for an unmanned autonomous helicopter using neural network and disturbance observer. Neurocomputing468: 296–305.
23.
WangBYuXMuLX, et al. (2019a) Disturbance observer-based adaptive fault-tolerant control for a quadrotor helicopter subject to parametric uncertainties and external disturbances. Mechanical Systems and Signal Processing120: 727–743.
24.
WangHChenM (2016) Trajectory tracking control for an indoor quadrotor UAV based on the disturbance observer. Transactions of the Institute of Measurement and Control38(6): 675–692.
25.
WangQZhaoQJ (2019) Rotor aerodynamic shape design for improving performance of an unmanned helicopter. Aerospace Science and Technology87: 478–487.
26.
WangTWuJWangY (2020) Adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems with time-varying input delay and full state constraints. IEEE Transactions on Fuzzy Systems28(12): 3432–3441.
27.
WangXHanLLiuJ (2021) Attitude and height tracking control of unmanned helicopters with disturbances via disturbance observer-based composite dynamic surface control. Transactions of the Institute of Measurement and Control43(14): 3294–3307.
28.
WangXYYuXLiSH (2019b) Composite block backstepping trajectory tracking control for disturbed unmanned helicopters. Aerospace Science and Technology85: 386–398.
29.
YanKWuQX (2022) Adaptive tracking flight control for unmanned autonomous helicopter with full state constraints and actuator faults. ISA Transactions128: 32–46.
30.
YanKChenMWuQX (2019a) Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults. Chinese Journal of Aeronautics32(10): 2299–2310.
31.
YanKChenMWuQX, et al. (2019b) Robust attitude fault-tolerant control for unmanned autonomous helicopter with flapping dynamics and actuator fault. Transactions of the Institute of Measurement and Control41(5): 1266–1277.
32.
YanKWuQXChenM (2018) LQR-based optimal tracking fault tolerant control for a helicopter with actuator faults. In: IEEE 7th data driven control and learning systems conference, Enshi, China, 25–27 May 2018, pp. 1022–1027. New York: IEEE.
33.
YangJCuiHLiSH, et al. (2018) Optimized active disturbance rejection control for DC-DC buck converters with uncertainties using a reduced-order GPI observer. IEEE Transactions on Circuits and Systems I: Regular Papers65(2): 832–841.
34.
ZhongMLiuYHuoZ (2008) H∞ fault estimation and accommodation for LTI systems. In: Proceedings of the Seventh world congress on intelligent control and automation, Chongqing, China, 25–27 June 2008, pp. 1806–1809. New York: IEEE.
