Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular...Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular method,nonlinear least square estimation,is shown to be strongly sensitive to outliers.In this paper,robust estimation of parameters using M-estimators is proposed,and their asymptotic properties are obtained under some regular conditions.The authors also provide a method to adjust Huber parameter automatically according to the observations.Moreover,a method is presented to estimate the initial values of parameters and state variables.The efficiency and robustness are well balanced in Huber estimators,which is demonstrated via numerical simulations and chlorides data analysis.展开更多
Unmanned Aerial Vehicles(UAVs)are highly nonlinear and sophisticated systems that demand precise trajectory tracking in environments with uncertainties and disturbances.This research presents advanced nonlinear,adapti...Unmanned Aerial Vehicles(UAVs)are highly nonlinear and sophisticated systems that demand precise trajectory tracking in environments with uncertainties and disturbances.This research presents advanced nonlinear,adaptive,and artificial intelligence-based control strategies for UAVs.Beyond simulation,the strategies are experimentally evaluated on a coupled Two Degree of Freedom(2-DOF)Twin-rotor MIMO System(TRMS).The proposed strategies include Sliding Mode Control(SMC),Super Twisting(ST),Back Stepping(BS),and Neuro-Adaptive SMC(NNSMC),all designed using a feedback linearized mathematical model of the system.System performance is enhanced by decoupling the TRMS into horizontal and vertical subsystems through Lie derivatives and diffeomorphism principles.A Uniform Robust Exact Differentiator(URED)estimates rotor speeds and recovers missing derivatives,while a nonlinear state feedback observer improves system observability and mitigates uncertainties and external wind gusts.Furthermore,ST and NNSMC-based laws reduce high-frequency oscillations in the control input of the first-order SMC law,resulting in improved transient response.The experimental results reveal that NNSMC significantly outperforms ST and BS in terms of trajectory tracking accuracy,transient performance,and integral performance indices for both pitch and yaw angles.These findings underscore the superior convergence performance and robustness of NNSMC,establishing it as a promising solution for precise TRMS control in real real-world environment.展开更多
基金supported by the Natural Science Foundation of China under Grant Nos.11201317,11028103,11231010,11471223Doctoral Fund of Ministry of Education of China under Grant No.20111108120002+1 种基金the Beijing Municipal Education Commission Foundation under Grant No.KM201210028005the Key project of Beijing Municipal Educational Commission
文摘Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular method,nonlinear least square estimation,is shown to be strongly sensitive to outliers.In this paper,robust estimation of parameters using M-estimators is proposed,and their asymptotic properties are obtained under some regular conditions.The authors also provide a method to adjust Huber parameter automatically according to the observations.Moreover,a method is presented to estimate the initial values of parameters and state variables.The efficiency and robustness are well balanced in Huber estimators,which is demonstrated via numerical simulations and chlorides data analysis.
基金supported by the National Natural Science Foundation of China(Grant No.12072027)the Key Research and Development Program of Henan Province(No.241111222000)the Henan Key Laboratory of General Aviation Technology(No.ZHKF-230201)
文摘Unmanned Aerial Vehicles(UAVs)are highly nonlinear and sophisticated systems that demand precise trajectory tracking in environments with uncertainties and disturbances.This research presents advanced nonlinear,adaptive,and artificial intelligence-based control strategies for UAVs.Beyond simulation,the strategies are experimentally evaluated on a coupled Two Degree of Freedom(2-DOF)Twin-rotor MIMO System(TRMS).The proposed strategies include Sliding Mode Control(SMC),Super Twisting(ST),Back Stepping(BS),and Neuro-Adaptive SMC(NNSMC),all designed using a feedback linearized mathematical model of the system.System performance is enhanced by decoupling the TRMS into horizontal and vertical subsystems through Lie derivatives and diffeomorphism principles.A Uniform Robust Exact Differentiator(URED)estimates rotor speeds and recovers missing derivatives,while a nonlinear state feedback observer improves system observability and mitigates uncertainties and external wind gusts.Furthermore,ST and NNSMC-based laws reduce high-frequency oscillations in the control input of the first-order SMC law,resulting in improved transient response.The experimental results reveal that NNSMC significantly outperforms ST and BS in terms of trajectory tracking accuracy,transient performance,and integral performance indices for both pitch and yaw angles.These findings underscore the superior convergence performance and robustness of NNSMC,establishing it as a promising solution for precise TRMS control in real real-world environment.
