This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy.

Active Disturbance Rejection Adaptive Control of Hydraulic Servo Systems

This paper proposes the adaptive extended state observer (AESO)-based active disturbance rejection control (ADRC) to deal with the uncertainties, both in the plant and in the sensors. The gain of ESO is automatically timely tuned to reduce the estimation errors of both states and “total disturbance” against the measurement noise. Furthermore, the satisfactory performance of the closed-loop system is achieved by compensation for uncertainties. This novel controller is applied to the air–fuel ratio (AFR) control of gasoline engine, which has large nonlinear uncertainties due to the unknown speed change, fuel film dynamics, etc. In addition, the measurement of AFR is polluted by sensor noise. The experimental results demonstrate that the proposed controller can ensure high deviation precision of AFR despite both uncertain dynamics and measurement noise. Moreover, the experimental comparison validates the effectiveness of the AESO's gain by which the performance of ADRC on mitigating uncertainties can be improved.

ADRC With Adaptive Extended State Observer and its Application to Air–Fuel Ratio Control in Gasoline Engines

This survey presents various methods of improving the overall estimation quality in the class of extended state observers (ESO), which estimate not only the conventional states of the system, but the acting disturbance as well. This type of observers is crucial in forming the active disturbance rejection control structure (ADRC), where the precision of online perturbation reconstruction and cancellation directly influences the robustness of the closed-loop control system. Various aspects of the observer-based disturbance estimation/rejection loop are covered by this work and divided into three categories, related with observer: structure, tuning, and working conditions. The survey is dedicated to researchers and practitioners who are interested in increasing the efficiency of their ADRC-based governing schemes.

Survey on methods of increasing the efficiency of extended state disturbance observers.

The output voltage regulation problem of a PWM-based dc-dc buck converter under various sources of uncertainties and disturbances is investigated in this paper via an optimized active disturbance rejection control (ADRC) approach. Aiming to practical implementation, a new reduced-order generalized proportional integral (GPI) observer is first designed to estimate the lumped (possibly time-varying) disturbances within the dc-dc circuit. By integrating the disturbance estimation information raised by the reduced-order GPI observer into the output prediction, an optimized ADRC method is developed to achieve optimized tracking performance even in the presence of disturbances and uncertainties. It is shown that the proposed controller will guarantee the rigorous stability of the closed-loop system, for any bounded uncertainties of the circuit, by appropriately choosing the observer gains and the bandwidth factor. Experimental results illustrate that the proposed control solution is characterized by improved robustness performance against various disturbances and uncertainties compared with traditional ADRC and integral model predictive control (MPC) approaches.

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Optimized Active Disturbance Rejection Control for DC-DC Buck Converters With Uncertainties Using a Reduced-Order GPI Observer

A nonlinear extended state observer based on fractional power functions

1key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; 2School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; 3Beijing Aerospace Automatic, Control Institute, Beijing 100854, China; 4Center for Advanced Control Technologies Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland Ohio 44115, USA.

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On the conceptualization of total disturbance and its profound implications

This paper considers the filtering problem for a class of multi-input multi-output systems with nonlinear time-varying uncertain dynamics, random process andmeasurement noise. An extended state based Kalman filter, with the idea of timely estimating the unknown dynamics, is proposed for better robustness and higher estimation precision. The stability of the proposed filter is rigorously proved for nonlinear time-varying uncertain system with weaker stability condition than the extended Kalman filter, i.e., the initial estimation error, the uncertain dynamics and the noises are only required to be bounded rather than small enough. Moreover, quantitative precision of the proposed filter is theoretically evaluated. The proposed algorithm is proved to be the asymptotic unbiased minimum variance filter for constant uncertainty. The simulation results of some benchmark examples demonstrate the feasibility and effectiveness of the method.

On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems

This paper studies the ability of ADRC for systems with unobservable and unmatched uncertainty. First the definition of observability for a class of uncertain nonlinear systems is given and an observability condition is proposed. Then the paper discusses how to transform the unobservable and unmatched uncertainty into the total disturbance, which is both observable and matched, so that the ability of ADRC can be achieved. The effectiveness of ADRC for the unobservable and unmatched uncertainty is shown by the simulations.

ADRC for systems with unobservable and unmatched uncertainty

http://ieeexplore.ieee.org/iel7/6882077/6895198/06897047.pdf

Wenyan Bai

Papers

ADRC With Adaptive Extended State Observer and its Application to Air–Fuel Ratio Control in Gasoline Engines

On the conceptualization of total disturbance and its profound implications

On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems

ADRC for systems with unobservable and unmatched uncertainty

The extended state filter for a class of multi-input multi-output nonlinear uncertain hybrid systems