Active disturbance rejection control strategy for airborne radar stabilization platform based on cascade extended state observer

Abstract

Purpose This paper aims to improve the anti-interference ability of the airborne radar stabilization platform, especially the ability to suppress continuous disturbance under complex air conditions to ensure the clarity and stability of airborne radar imaging. Design/methodology/approach This paper proposes a new active disturbance rejection control (ADRC) strategy based on the cascade extended state observer (ESO) for airborne radar stabilization platform, which adopts two first-order ESOs to estimate the angular velocity value and the angular position value of the stabilized platform. Then makes the error signal which subtracts the estimated value of ESO from the output signal of the tracking-differentiator as the input signal of the nonlinear state error feedback (NLSEF), and according to the output signal of the NLSEF and the value which dynamically compensated the total disturbances estimated by the two ESO to produce the final control signal. Findings The simulation results show that, compared with the classical ADRC, the ADRC based on the cascade ESO not only estimates the unknown disturbance more accurately but also improves the delay of disturbance observation effectively due to the increase of the order of the observer. In addition, compared with the classical PID control and the classical ADRC, it has made great progress in response performance and anti-interference ability, especially in the complex air conditions. Originality/value The originality of the paper is the adoption of a new ADRC control strategy based on the cascade ESO to ameliorate the anti-interference ability of the airborne radar stabilization platform, especially the ability to suppress continuous interference under complex air conditions.

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

“…Then (4) can also be written in the following state space equation as (5) where θ = x 1 , . θ = w = x 2 and w is the mechanical angular velocity.…”

“…The servo mechanism is susceptible to the influence of external interference such as the attitude of the carrier, vibration, airflow and the follow-up of the target position when the radar is tracking for the target. Therefore, the radar position servo system is supposed to have high static stability, dynamic response capability and strong resistance disturbance capability [4,5]. Traditional PID control is currently widely used in radar position servo systems, it has a tracking lag and is easily affected by external disturbances since dead zone and friction nonlinearities widely exist in radar servo systems [6] so it cannot meet high precision control requirements.…”

Interference compensation discrete control based on memory data for a class of nonlinear systems

“…So far, most use observer or observer in combination with other techniques (e.g., backstepping technology, sliding mode technology) for interference compensation control. For example: Deng 2 designed a dynamic compensation control to compensate the uncertainty disturbance in each state quantity with combining backstepping technology and extended state observer; Zhang 3 studied the disturbance observer based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances; Mei 4 proposed a new active disturbance rejection control strategy based on the cascade extended state observer for airborne radar stabilization platform. Shi 5 presented an nonlinear cascade control based the reduced order extended state observer for the high‐response proportional solenoid valve.…”

An enhanced sensorless control based on active disturbance rejection controller for a PMSM system: design and hardware implementation

“…So developing effective control methods to achieve excellent speed control performance has gradually become a significant issue for PMSMs in the past few decades (Meng et al , 2019; Wang et al , 2019). Nowadays, a variety of advanced control strategies have been applied to PMSMs, such as sliding mode control (SMC) (Fang et al , 2020; Huang et al , 2019; Wang et al , 2017), H-infinity control (Hans and Ghosh, 2020), model predictive control (MPC) (Li et al , 2020; Wang et al , 2020; Yan et al , 2019), neural network control (NNC) (Cheng and Chen, 2020), active disturbance rejection control (ADRC) (Liu et al , 2019; Mei and Yu, 2020) and so on, which not only improve the dynamic and steady state performances of the PMSMs but also enrich the control methods of the PMSMs.…”