## 1 Introduction
In view of this, ADRC regards state observer technology as its core part so that ADRC can simplify the controlled system into a set of disturbed systems including external disturbance input, internal disturbance, uncertainty, and un-modeled parts of the system model, which will be seen as all disturbance and estimated by the observer. Although this observer can estimate the disturbance for the controller and reduce its influence on the control effect, it can no longer meet the requirements of scholars for the control accuracy with the progress of technology [20; 21]. Artificial neural networks are mathematical models that mimic the structure and function of brain neural networks. Because of its powerful learning ability and adaptability, neural networks are very suitable for approximating unknown functions. This has been widely applied by scholars to the approximation of unknown uncertainties in linear or nonlinear control systems [22; 23]. Based on the arrangement of the TD and conversion process, the error signal can be tracked after the conversion process. Using this signal and its differential signal, it can be controlled by nonlinear states error feed-back (NLSEF) in Reference [24]. However, the traditional control rate does not have good control effects on system uncertainty, disturbance, and unmolded dynamics. Even if the neural network state observer (NNSO) is used to compensate for all the uncertainties of the system, it has certain modeling uncertainty which needs to be improved. For the above problem, some scholars have found that sliding mode control (SMC) can solve it well [25; 26; 27]. As an alternative to the typical robust control scheme, SMC is a typical control method. The advantage of SMC is that it can overcome the uncertainty of the system and has strong robustness to disturbance and un-modeled dynamics, especially for the control of nonlinear systems. Some scholars have studied the comparison of SMC and ADRC [28]. Reference [28] concerned with the boundary feedback stabilization of a one-dimensional Euler-Bernoulli beam equation with the external disturbance flowing to the control end, it is shown that the external disturbance can be attenuated in the sense that the resulting closed-loop system under the extended state feedback tends to any arbitrary given vicinity of zero as the time goes to infinity. The advantage of SMC is that it can overcome the uncertainty of the system, and has strong robustness to interference and unmolded dynamics. This can make up for the uncertainty of NNSO itself. Although the stability of the system can be improved through the above analysis, due to the controlled plant, the particleboard glue mixing and dosing system is a time-delay system in low frequency working conditions (15 HZ input frequency), and it is often difficult to achieve satisfactory results. The time delay will bring additional phase lag, and the phase lag will increase with the increase of frequency. To solve the problem of time-delay, a first-order approximation for the dead time is used in Reference [29]. In this way, further precise control can be achieved under the low frequency conditions of the glue application. Considering that ADRC theory has not been applied in particleboard production, we propose an improved active disturbance rejection controller for the particleboard glue mixing and dosing system in this paper. The main contributions of this paper are summarized as follows:  
1. The process of mixing and dosing the glue for particleboard is introduced and so is the composition of the system. Then we establish the state space equation. 2. We construct an improved controller using ADRC approach. The improved NNSO is utilized to observe disturbance for control compensation and estimate the system state variables. To simplify the controller, the improved TD is used to achieve smooth transmission of differential signals and realize optimal configuration of closed-loop system transition processes. The SMC is introduced to improve the robustness for the disturbance not observed by NNSO.