Disturbance rejection control of airborne radar stabilized platform based on active disturbance rejection control inverse estimation algorithm

Purpose This paper aims to study a disturbance rejection controller to improve the anti-interference capability and the position tracking performance of airborne radar stabilized platform that ensures the stability and clarity of synthetic aperture radar imaging. Design/methodology/approach This study proposes a disturbance rejection control scheme for an airborne radar stabilized platform based on the active disturbance rejection control (ADRC) inverse estimation algorithm. Exploiting the extended state observer (ESO) characteristic, an inversely ESO is developed to inverse estimate the unmodeled state and extended state of the platform system known as total disturbances, which greatly improves the estimation performance of the disturbance. Then, based on the inverse ESO result, feedback the difference between the output of the tracking differentiator and the inverse ESO result to the nonlinear state error feedback controller (NLSEF) to eliminate the effects of total disturbance and ensure the stability of the airborne radar stabilized platform. Findings Simulation experiments are adopted to compare the performance of the ADRC inverse estimation algorithm with that of the proportional integral derivative controller which is one of the mostly applied control schemes in platform systems. In addition, classical ADRC is compared as well. The results have shown that the ADRC inverse estimation algorithm has a better disturbance rejection performance when disturbance acts in airborne radar stabilized platform, especially disturbed by continuous airflow under some harsh air conditions. Originality/value The originality of this paper is exploiting the ESO characteristic to develop an inverse ESO, which greatly improves the estimation performance of the disturbance. And the ADRC inverse estimation algorithm is applied to ameliorate the anti-interference ability of the airborne radar stabilization platform, especially the ability to suppress continuous interference under complex air conditions.

On the identifiability of soil hydraulic parameters in lysimeter experiments: a Bayesian perspective

<p>Groundwater recharge through the vadose zone is an important yet hard to quantify variable. It is estimated from lysimeter experiments or mathematical modelling. For the simulation of groundwater recharge rates with a physically based model soil hydraulic properties (SHPs) have to be inversely estimated because SHPs from laboratory experiments can only be poorly transferred to field conditions. Still, also the inverse estimation of SHPs, is associated with experimental and modeling uncertainties that propagate into the recharge prediction. New methods are thus required improving the inverse estimation of SHPs and reducing the uncertainty in groundwater recharge prediction. Therefore, this study aims to investigate how the assimilation of different types of soil water measurements for the inverse estimation of SHPs with the HYDRUS-1D software affects the estimated uncertainty. For this purpose, observations from a monolithic lysimeter experiment (i.e. lysimeter outflow, soil pressure head and volumetric soil water content at two different depths) have been combined in the different modeling scenarios and coupled with a Bayesian analysis to inversely estimate SHPs and assess their uncertainty. Posterior predictive checks showed that the simultaneous assimilation of outflow and soil pressure head led to the smallest uncertainty in groundwater recharge prediction. This represented a reduction in uncertainty compared to assimilating lysimeter outflow alone. Additional information provided by measurements of soil water content resulted in a reduced parameter uncertainty for residual and saturated water content, however, it did not further reduce the uncertainty in recharge prediction. Overall, this study shows the applicability of a Bayesian analysis for determining uncertainties in the inverse estimation of SHPs with lysimeter data and for the quantification of the associated uncertainty in groundwater recharge prediction. Based on our results for the investigated site, we recommend simultaneous assimilation of lysimeter outflow and soil pressure head measurements to minimize uncertainty in groundwater recharge prediction. However, a more comprehensive analysis is required to make a generally valid recommendation for other soils or climates.</p><p> </p>