Active disturbance rejection control

Active disturbance rejection control (or ADRC) inherits from proportional–integral–derivative (PID). It embraces the power of nonlinear feedback and puts it to full use. It is a robust control method that is based on extension of the system model with an additional and fictitious state variable, representing everything that the user does not include in the mathematical description of the plant. This virtual state (sum of internal and external disturbances, usually denoted as a 'total disturbance') is estimated online with a state observer and used in the control signal in order to decouple the system from the actual perturbation acting on the plant. This disturbance rejection feature allows user to treat the considered system with a simpler model, since the negative effects of modeling uncertainty are compensated in real time. As a result, the operator does not need a precise analytical description of the system, as one can assume the unknown parts of dynamics as the internal disturbance in the plant. Robustness and the adaptive ability of this method makes it an interesting solution in scenarios where the full knowledge of the system is not available. Active disturbance rejection control (or ADRC) inherits from proportional–integral–derivative (PID). It embraces the power of nonlinear feedback and puts it to full use. It is a robust control method that is based on extension of the system model with an additional and fictitious state variable, representing everything that the user does not include in the mathematical description of the plant. This virtual state (sum of internal and external disturbances, usually denoted as a 'total disturbance') is estimated online with a state observer and used in the control signal in order to decouple the system from the actual perturbation acting on the plant. This disturbance rejection feature allows user to treat the considered system with a simpler model, since the negative effects of modeling uncertainty are compensated in real time. As a result, the operator does not need a precise analytical description of the system, as one can assume the unknown parts of dynamics as the internal disturbance in the plant. Robustness and the adaptive ability of this method makes it an interesting solution in scenarios where the full knowledge of the system is not available. Tracking differentiator solves the trading off with Rapidity and Overstrike. Besides, it improves the controller's anti-noise ability. The convergence of ADRC is proved by Guo and his students.