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

Abstract

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.

Active disturbance rejection control for optoelectronic stabilized platform based on model-assisted double extended state observers

“…According to equation (3), f ( x 1 , x 2 , t ) is the function of inner disturbance, and Tf′ is external disturbance. The classical ESO considers f ( x 1 , x 2 , t ) and Tf′ as an extended state together, and equation (3) can be rewritten as follows (Mei and Yu, 2021): where x3=f(x1,x2,t)+Tf′ is the total disturbances, and w 1 ( t ) is the derivative of x 3 . By establishing an ESO for equation (7) through a nonlinear function, the classical ESO can be written as follows: where fal(e,α,σ)=true{lefteσ1−α,true|etrue|≤σ|e|αsign(e),true|etrue|>σ as in Han(2009), σ is the length of the linear segment interval, e is the error between the estimated state and the actual output, and α is a constant between 0 and 1. z 1 , z 2 , z 3 are the estimated values of x 1 , x 2 , x 3, respectively, and β 01 , β 02 , β 03 are the parameter values of the ESO.…”

“…Once the input is a signal with noise, the TD has a superior filtering effect in suppressing the noise. 3) can be rewritten as follows (Mei and Yu, 2021):…”

“…Existing references pay more attention to structural optimization but lack consideration of the gain of the ESO. Mei and Yu (2020, 2021) proposed two cascade ESOs for an airborne radar stabilization platform. Zhao and Liu (2020) developed three disturbance observers to suppress shear deformation and elastic oscillation.…”

An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform

“…The article also presents an adaptive neural network to approximate the uncertainty and unknown disorders of the system and improve control performance. An anti-disturbance control scheme for an airborne radar stabilization platform based on the inverse estimation algorithm of self-rejecting control (ADRC) is used to solve the stability and clarity problems of radar imaging [ 11 ]. An improved dynamic variational differential evolution algorithm (DMDE) is proposed to optimize the PID control parameters and is validated by simulation for several standard industrial-controlled object models [ 12 ].…”