The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this article, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a dc–dc buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise overamplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications.

Active disturbance rejection control (ADRC) as a standalone motion solution has been made available in recent years on various industrial platforms. The idea of ADRC, however, can be integrated with the existing control technologies seamlessly, as shown in this paper. A modularized ADRC design is proposed in this particular work for set-point tracking task in motion control, such that better uncertainty rejection can be obtained without any change in the existing proportional-derivative control with a linear observer. It is proven that a certain extended state of the observer, i.e., the integration of the observer error, can serve as an estimation for the “total disturbance” in low-frequency range by only tuning the augmented gain. This enables the estimation and cancellation of the “total disturbance” to be incorporated into the existing control loop. Also, a comparison between the methods with and without such “module” is discussed. The proposed ADRC is implemented and validated experimentally using a laboratory manipulator, where the desired set-point tracking performance in position control is achieved under unknown mass variations and sudden external disturbances.

The potential of active disturbance rejection control (ADRC) as a viable solution to industrial control has become increasingly evident after the recent adoptions by major industrial concerns, chiefly because of its ability of disturbance rejection and its independence of a detailed mathematical model of the plant. With the controller and observer bandwidths in ADRC largely determined a priori by the design specifications, and with the order of the plant largely known for major classes of industrial processes, the only parameter the user needs is the gain in the input channel that characterizes the proportional relation between the input signal and the derivative of the process output of certain order. This normally slow changing process parameter is denoted here as the critical gain parameter (CGP). This paper is concerned with the task of estimating CGP and regularly updating it in the ADRC, thus making it possible for the controller to be set up, tuned, and commissioned with a single stroke of a key by the industry personnel. The proposed approach integrates the classic ADRC with an extremum seeking-based parameter estimation of the CGP and it greatly expands the operation range and the robustness of the ADRC-based approach, relieving the designer from the tedious task of empirical tuning. An illustrative simulation example is provided to validate the proposition.