Parameter Identifying Disturbance Rejection Control With Asymptotic Error Convergence

Patelski, Radosław; Pazderski, Dariusz

doi:10.1109/lra.2023.3339942

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…In addition, although an ESO can observe the total disturbance, there is always an error between the observed and actual values of the total disturbance, leaving the system with a residual error even when reduced to the integrator series type. In current ADRC designs, advanced control law design methods are often combined with an ESO, such as backstepping control [37], sliding mode control [38], and adaptive control [39,40]. This residual error has not been specifically investigated.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Phase-Compensated Active Disturbance Rejection Control with an Improved Backstepping Strategy for Electro-Optical Tracking Systems

Zhuang,

Li,

Wang

et al. 2024

Actuators

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A multi-channel phase-compensated active disturbance rejection control (MPADRC) incorporating an improved backstepping strategy is proposed in this paper to handle the phase lag in the extended state observer (ESO) and the residual uncertainty in the system. Firstly, a multi-channel phase-compensated ESO (MPESO) is constructed by adding phase-advanced networks to all output channels of the ESO, which allows disturbances and system states to be compensated and feedback in a more timely manner, respectively. Then, to estimate and offset the residual uncertainty in the system, an improved backstepping control method is employed and a Lyapunov function is designed to verify the convergence of the error between the estimated and actual values of the residual uncertainty. After that, the improved backstepping control is combined with MPADRC, and comparisons with the conventional linear active disturbance rejection control (LADRC) are conducted for a range of cases. Finally, on an inertial stabilization platform in the electro-optical tracking system (ETS), simulation and experimental results verified the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Multi-Channel Phase-Compensated Active Disturbance Rejection Control with an Improved Backstepping Strategy for Electro-Optical Tracking Systems

Zhuang,

Li,

Wang

et al. 2024

Actuators

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Underwater Manipulator Linear Active Disturbance Rejection Control Based on Hierarchical Cascaded Linear Extended State Observer

Xu,

Gao,

Wang

et al. 2024

IEEE Access

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Adaptive Active Disturbance Rejection Control with Recursive Parameter Identification

Michalski,

Mrotek,

Retinger

et al. 2024

Electronics

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This paper presents a new adaptive modification of active disturbance rejection control (ADRC) with parameter estimation based on a recursive least-squares (RLS) method. The common ADRC used in many applications relies on the simple approach, which assumes the simplification of the object into an integral chain form. However, this model-free ADRC does not guarantee the stability of a closed-loop system in the presence of noticeable modeling uncertainties, so it is compared in this paper to another approach, in which the linear part of the system is included in the ADRC framework (generalized ADRC). This incorporation of the model is examined in the paper for a wide range of model and controller parameters, considering also the presence of external disturbances as well as parameter uncertainties, pointing out the limitations of fixed-gain algorithms. Then, the adaptive modification of the model-based ADRC is proposed, which is equipped with a real-time estimation of model parameters by means of the RLS method in continuous time. The stability conditions of the proposed modification of the algorithm in the closed control loop are also analyzed. It can be concluded that, under appropriate conditions, the inclusion of information about known plant parameters into the ADRC can noticeably improve the conditions of the control system. The proposed adaptive model-based approach enables quality improvement during the control process even with initially unknown parameters, for time-varying parameters, and in the presence of parametric uncertainties and external disturbances. The tests were performed on a real plant—the task of controlling the angular velocity of the direct current (DC) motor was considered.

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Parameter Identifying Disturbance Rejection Control With Asymptotic Error Convergence

Cited by 3 publications

References 32 publications

Multi-Channel Phase-Compensated Active Disturbance Rejection Control with an Improved Backstepping Strategy for Electro-Optical Tracking Systems

Multi-Channel Phase-Compensated Active Disturbance Rejection Control with an Improved Backstepping Strategy for Electro-Optical Tracking Systems

Underwater Manipulator Linear Active Disturbance Rejection Control Based on Hierarchical Cascaded Linear Extended State Observer

Adaptive Active Disturbance Rejection Control with Recursive Parameter Identification

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