# Drum Water Level Control Based on Improved ADRC
## 1 Introduction  
Boilers are widely used in the chemical industry, oil refining, power generation, and other industrial production activities. It is an important power source of industrial production. Normal drum water level is one of the preconditions for safe operation of boilers. At present, two-impulse and three-impulse cascade controls are commonly used in industry. Due to the changeable operation conditions of the equipment, the water level regulation process of the boiler has the characteristics of non-linearity, instability, time delay, and strong coupling. Therefore, using conventional PID control it is often difficult to meet the requirements [1; 2]. Advanced control strategies are more and more frequently used in water level control of the boiler drum, and have made great achievements. [PERSON] [3] proposed a self-adaptive fuzzy-PID control method by combining PID theory with fuzzy control. [PERSON] [4] introduced the Linear Quadratic Regulator (LQR) algorithm into conventional water level control strategy, and the control effect was improved. [PERSON] [5] introduced the double heuristic dynamic optimization theory into the water level control, which enhanced the robustness to a certain extent. [PERSON] [6] combined genetic algorithms with PID theory and calculated the optimal value of PID parameters by genetic algorithm. [PERSON] [7] used the prediction function and combined this method with the PID method to enhance the dynamic stability of the system. However, the performance of these methods is not excellent, and it cannot fundamentally solve the problems of non-linearity, instability, time delay, and strong coupling. [PERSON] [8] presented a new design algorithm for the decentralized output feedback control problem of large-scale interconnected systems. The nonlinear function is assumed to be bounded bya quadratic inequality, and a decentralized estimated state feedback controller and a decentralized observer are designed for each subsystem, based on linear matrix inequalities. The controller is evaluated on a natural circulation drum boiler and the effect is good. [PERSON] [9] proposed that the sliding mode observer could accurately estimate the speed and position of the rotor, and could be used in the maximum power point tracking algorithm. This method can improve the system reliability of small wind turbines, and be used in heating water application. Aiming at nonlinear dynamics problems caused by saturation of control voltage of active magnetic bearing (AMB), [PERSON] [10] implemented the feedback design of Lyapunov sliding mode observer (LSMO). Nonlinear observer was used to estimate magnetic flux and rotor mass velocity, which greatly reduced the cost. However, these methods are extremely sensitive to measuring noise. The core idea of active disturbance rejection control (ADRC) [11; 12; 13] is that the internal uncertainties (steady or time-varying, linear or non-linear) and external uncertainties (external disturbances) of the system are regarded as the total disturbances. The extended state observer (ESO) was constructed to estimate and compensate the total disturbances in real time, so as to obtain strong antidisturbance ability. [PERSON] [14] realizes the control of drum water level by using a cascade three-impulse control system composed of a non-linear ADRC, but the parameter setting is very complicated and the performance is not very good. Linear active disturbance rejection control (LADRC) [15; 16] uses the estimated error and its derivatives to provide linear state feedback, and converts the design of ESO and state feedback into the selection of two parameters--controller bandwidth (\(\omega_{c}\)) and observer bandwidth (\(\omega_{0}\)). It greatly simplifies the structure and parameter tuning of ADRC, and makes its application in industry possible. With the development of ADRC theory and technology, it has been widely used in various practical control problems.