The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as ex...The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.展开更多
Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location r...Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle.Since the noise of the vision location result is affected by external environment,the variance of the noise is uncertain.However,in previous researches,the variance is usually set as a fixed empirical value,which will lower the accuracy of the motion estimation.The main contribution of this paper is that we proposed a novel adaptive noise variance identification(ANVI) method,which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise.The adaptively identified variance is used in the Kalman filter for more accurate motion estimation.The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system.The results illustrate the effectiveness of the method.展开更多
基金supported by the National Natural Science Foundation of China(61273127U1534208)+2 种基金the Key Program of National Natural Science Foundation of China(61533014)the Key Laboratory for Fault Diagnosis and Maintenance of Spacecraft in Orbit(SDML-OF2015004)the Science and Technology Preject of Shaanxi Province(2016GY-108)
文摘The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.
基金supported by National Science and Technology Major Projects of the Ministry of Science and Technology of China:ITER(No.2012GB102007)
文摘Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle.Since the noise of the vision location result is affected by external environment,the variance of the noise is uncertain.However,in previous researches,the variance is usually set as a fixed empirical value,which will lower the accuracy of the motion estimation.The main contribution of this paper is that we proposed a novel adaptive noise variance identification(ANVI) method,which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise.The adaptively identified variance is used in the Kalman filter for more accurate motion estimation.The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system.The results illustrate the effectiveness of the method.
