Disturbances rejection based on sliding mode control

Purpose The purpose of this paper aims to investigate an effective algorithm for different types of disturbances rejection. New dynamics are designed based on disturbance. Observer-based sliding mode control (SMC) technique is used for approximation the disturbances as well as to stabilize the system effectively in presence of uncertainties. Design/methodology/approach This research work investigates the disturbances rejection algorithm for fixed-wing unmanned aerial vehicle. An algorithm based on SMC is introduced for disturbances rejection. Two types of disturbances are considered, the constant disturbance and the sinusoidal disturbance. The comprehensive lateral and longitudinal models of the system are presented. Two types of dynamics, the dynamics without disturbance and the new dynamics with disturbance, are presented. An observer-based algorithm is presented for the estimation of the dynamics with disturbances. Intensive simulations and experiments have been performed; the results not only guarantee the robustness and stability of the system but the effectiveness of the proposed algorithm as well. Findings In previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. Practical implications As the stability is always important for flight, so the algorithm proposed in this research guarantees the robustness and rejection of disturbances, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In the previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. An observer-based SMC not only approximates the different disturbances and also these disturbances are rejected in order to guarantee the effectiveness and robustness.

[1]  Oscar Salas-Peña,et al.  Robust flight control for a fixed-wing unmanned aerial vehicle using adaptive super-twisting approach , 2014 .

[2]  João Borges de Sousa,et al.  MULTI-UAV PLATFORM FOR INTEGRATION IN MIXED-INITIATIVE COORDINATED MISSIONS , 2006 .

[3]  Guanglin Li,et al.  Fuzzy Approximation-Based Adaptive Backstepping Control of an Exoskeleton for Human Upper Limbs , 2015, IEEE Transactions on Fuzzy Systems.

[4]  Alessandro Astolfi,et al.  Non-linear and adaptive flight control of autonomous aircraft using invariant manifolds , 2010 .

[5]  Wenchao Xue,et al.  Active disturbance rejection control: methodology and theoretical analysis. , 2014, ISA transactions.

[6]  Rogelio Lozano,et al.  Adaptive Trajectory Following for a Fixed-Wing UAV in Presence of Crosswind , 2013, J. Intell. Robotic Syst..

[7]  Dan Bugajski,et al.  Dynamic inversion: an evolving methodology for flight control design , 1994 .

[8]  Daibing Zhang,et al.  Active disturbance rejection controller for small fixed-wing UAVs with model uncertainty , 2015, 2015 IEEE International Conference on Information and Automation.

[9]  Miguel A. Olivares-Méndez,et al.  Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers , 2015, Sensors.

[10]  Wen-Hua Chen,et al.  Path‐following control for small fixed‐wing unmanned aerial vehicles under wind disturbances , 2012 .

[11]  Peter I. Corke,et al.  Vertical Infrastructure Inspection Using a Quadcopter and Shared Autonomy Control , 2012, FSR.

[12]  Mojtaba Ahmadieh Khanesar,et al.  Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks , 2017 .

[13]  Andrea Serrani,et al.  Adaptive restricted trajectory tracking for a non-minimum phase hypersonic vehicle model , 2012, Autom..

[14]  Fang Wang,et al.  Quasi-continuous high-order sliding mode controller design for reusable launch vehicles in reentry phase , 2013 .

[15]  Zhengtao Ding,et al.  Consensus Disturbance Rejection With Disturbance Observers , 2015, IEEE Transactions on Industrial Electronics.

[16]  H. Jin Kim,et al.  Neural Networks Adaptive Support Vector Regression for Uav Flight Control , 2022 .

[17]  José Ángel Acosta,et al.  Adaptive Control for Aircraft Longitudinal Dynamics with Thrust Saturation , 2015 .

[18]  Mazen Farhood,et al.  Optimal control of a small fixed-wing UAV about concatenated trajectories , 2015 .

[19]  José de Jesús Rubio,et al.  An observer with controller to detect and reject disturbances , 2014, Int. J. Control.

[20]  Zhengtao Ding,et al.  Adaptive rejection of non-linear disturbances in extended non-linear output feedback systems , 2008, Int. J. Control.

[21]  Wenya Zhou,et al.  Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control , 2014 .

[22]  Houria Siguerdidjane,et al.  On the guidance of a UAV under unknown wind disturbances , 2014, 2014 IEEE Conference on Control Applications (CCA).

[23]  Joao P. Hespanha,et al.  Vision-based target tracking with a small UAV: Optimization-based control strategies , 2014 .

[24]  J. Karl Hedrick,et al.  Linear Tracking for a Fixed-Wing UAV Using Nonlinear Model Predictive Control , 2009, IEEE Transactions on Control Systems Technology.

[25]  Yangquan Chen,et al.  A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification , 2014, J. Intell. Robotic Syst..