Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer
Restricted accessResearch articleFirst published online June, 2020
Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer
In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor trajectory tracking and obstacle avoidance. Comparative simulations and Monte Carlo tests are carried out to demonstrate the effectiveness and robustness of the CGWO-based backstepping control scheme and virtual target guidance approach.
AlexisKNikolakopoulosGTzesA (2012) Model predictive quadrotor control: attitude, altitude and position experimental studies. IET Control Theory & Applications6(12): 1812–1827.
2.
BaharnazBAhmadF (2019) Trajectory tracking for quadrotor UAV transporting cable-suspended payload in wind presence. Transactions of the Institute of Measurement and Control41(5): 1243–1255.
3.
BouadiHMora-CaminoFAC (2017) Modeling and adaptive flight control for quadrotor trajectory tracking. Journal of Aircraft55(1): 1–16.
4.
CaiZLouJZhaoJ, et al. (2019) Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control. Mechanical Systems and Signal Processing128: 636–654.
5.
ChenFLeiWZhangK, et al. (2016) A novel nonlinear resilient control for a quadrotor UAV via backstepping control and nonlinear disturbance observer. Nonlinear Dynamics85(2): 1281–1295.
6.
ChangKXiaYHuangK, et al. (2016) Obstacle avoidance and active disturbance rejection control for a quadrotor. Neurocomputing190: 60–69.
7.
ChovancováAFicoTHubinskýP, et al. (2016) Comparison of various quaternion-based control methods applied to quadrotor with disturbance observer and position estimator. Robotics & Autonomous Systems79: 87–98.
8.
DuBPolyakovAZhengG, et al. (2018) Quadrotor trajectory tracking by using fixed-time differentiator. International Journal of Control92(12): 2854–2868.
9.
EskandarpourASharfI (2019) A constrained error-based MPC for path following of quadrotor with stability analysis. Nonlinear Dynamics: 1–20.
10.
GreatwoodCRichardsAG (2019) Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control. Autonomous Robots43(7): 1681–1693.
11.
GuoYWuMTangK, et al. (2019) Position deceptive tracking controller and parameters analysis via error characteristics for unmanned aerial vehicle. International Journal of Advanced Robotic Systems16(1): 1–12.
12.
IaconoMSgorbissaA (2018) Path following and obstacle avoidance for an autonomous UAV using a depth camera. Robotics & Autonomous Systems106: 38–46.
13.
JungS (2014) An impedance force control approach to a quad-rotor system based on an acceleration-based disturbance observer. Journal of Intelligent & Robotic Systems73(1–4): 175–185.
14.
KanellakopoulosIKokotovićPMorseAS (1991) Systematic design of adaptive controllers for feedback linearizable systems. IEEE Transactions on Automatic Control36(11): 1241–1253.
15.
KocerBBTjahjowidodoTSeetGGL (2018) Centralized predictive ceiling interaction control of quadrotor VTOL UAV. Aerospace Science & Technology76: 455–465.
16.
LazimIMHusainARMohamedZ, et al. (2019) Disturbance observer-based formation tracking control of multiple quadrotors in the presence of disturbances. Transactions of the Institute of Measurement and Control 41: 4129–4141.
17.
LimHParkJLeeD, et al. (2012) Build your own quadrotor: open-source projects on unmanned aerial vehicles. IEEE Robotics & Automation Magazine19(3): 33–45.
18.
LiuYMaJTuH (2018) Robust command filtered adaptive backstepping control for a quadrotor aircraft. Journal of Control Science and Engineering2018: 1–9.
19.
LiuYWangQDongC, et al. (2019) Time-varying formation control for unmanned aerial vehicles with external disturbances. Transactions of the Institute of Measurement and Control41: 3777–3786.
20.
LouWXiaoG (2016) Adaptive trajectory tracking control using reinforcement learning for quadrotor. International Journal of Advanced Robotic Systems13(1).
21.
MirjaliliSMirjaliliSMLewisA (2014) Grey wolf optimizer. Advances in Engineering Software69: 46–61.
22.
PanZChiCZhangJ (2018) Nonlinear attitude control of tiltrotor aircraft in helicopter mode based on ADRSM theory. In: 37th Chinese Control Conference, Wuhan, China, 25–27 July 2018, pp. 9962–9967.
23.
PoundsPMahonyRCorkeP (2010) Modelling and control of a large quadrotor robot. Control Engineering Practice18(7): 691–699.
24.
QiuBWangGFanY, et al. (2018). Robust adaptive trajectory linearization control for tracking control of surface vessels with modeling uncertainties under input saturation. IEEE Access7: 5057–5070.
25.
SantosMCPRosalesCDSarapuraJA, et al. (2019) An adaptive dynamic controller for quadrotor to perform trajectory tracking tasks. Journal of Intelligent & Robotic Systems93(1–2): 5–16.
26.
ShiDDaiXZhangX, et al. (2017) A practical performance evaluation method for electric multicopters. IEEE/ASME Transactions on Mechatronics22(3): 1337–1348.
27.
SumantriBUchiyamaNSanoS (2016) Least square based sliding mode control for a quad-rotor helicopter and energy saving by chattering reduction. Mechanical Systems & Signal Processing66–67: 769–784.
28.
ThanhHLNNPhiNNHongSK (2018) Simple nonlinear control of quadcopter for collision avoidance based on geometric approach in static environment. Advanced Robotic Systems15(2): 1–17.
29.
WangRLiuJ (2017) Adaptive formation control of quadrotor unmanned aerial vehicles with bounded control thrust. Chinese Journal of Aeronautics30(2): 807–817.
30.
WangRLiuJ (2018) Trajectory tracking control of a 6-DOF quadrotor UAV with input saturation via backstepping. Journal of the Franklin Institute355(7): 3288–3309.
31.
WangTXieWZhangY (2014) Sliding mode reconfigurable control using information on the control effectiveness of actuators. Journal of Aerospace Engineering27(3): 587–596.
32.
WuGSongSSunJ (2018) Adaptive dynamic surface control for spacecraft terminal safe approach with input saturation based on tracking differentiator. International Journal of Control, Automation and Systems16(3): 1129–1141.
33.
WuYLiG (2018) Adaptive disturbance compensation finite control set optimal control for PMSM systems based on sliding mode extended state observer. Mechanical Systems and Signal Processing98: 402–414.
34.
ZhaoDYangD (2016) Model-free control of quad-rotor vehicle via finite-time convergent extended state observer. International Journal of Control Automation & Systems14(1): 242–254.
35.
ZhouLZhangLSheH, et al. (2019) Quadrotor UAV flight control via a novel saturation integral backstepping controller. Automatika60(2): 193–206.
36.
ZuoZRuP (2014) Augmented L1 adaptive tracking control of quad-rotor unmanned aircrafts. IEEE Transactions on Aerospace & Electronic Systems50(4): 3090–3101.
