Disturbance-Improved Model-Free Adaptive Prediction Control for Discrete-Time Nonlinear Systems with Time Delay

Ji, Honghai; Wei, Yuzhou; Fan, Lingling; Liu, Shida; Wang, Yulin; Wang, Li

doi:10.3390/sym13112128

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…Model predictive control (MPC) was initially employed in the process industry with slow dynamic in the 1970s [1][2][3]. The main concept centers around solving an optimization problem online by predicting the future behavior of the control plant [4,5]. With the tremendous development of digital processors, the computational capability of the controllers has been significantly increased in the recent decade, which enables MPC to be applied in power electronics applications, e.g., railway transportation and wind turbines [6,7].…”

Section: Introductionmentioning

confidence: 99%

Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Xie

et al. 2022

Symmetry

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Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.

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Section: Introductionmentioning

confidence: 99%

Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Xie

et al. 2022

Symmetry

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Difference Estimator Based Data-Driven Predictive Anti-Disturbance Control for Input-Delayed Nonlinear Systems with Unknown Dynamics

Du,

Hao,

Liu

et al. 2024

Lecture Notes in Electrical Engineering

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Design and Analysis of an Active Disturbance Rejection Robust Adaptive Control System for Electromechanical Actuator

Chen

Zhu

et al. 2021

Actuators

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Airline electromechanical actuators (EMAs), on the task of controlling flight surfaces, hold a great promise with the development of more- and all-electric aircraft. Notwithstanding, the deficiencies in both robustness and adaptability of control algorithms prevent EMAs from extensive use. However, the state-of-the-art control schemes fail to precisely compensate the system nonlinear uncertainties of servo control. In this paper, from the innovation point of view, we tend to put forward the foundation of devising an active disturbance rejection robust adaptive control (ADRRAC) strategy, whose main purpose is to deal with the position servo control of EMA. Specifically, an adaptive control law is designed and deployed for resolving not only the nonlinear disturbance, but also the parameter uncertainties. In addition, an extended disturbance estimator is employed to estimate the external disturbance and thus eliminate its impact. The proposed controlling algorithm is deemed best able to address the external disturbance based on the nonlinear uncertainty compensation. With the input parameters and control commands, the ADRRAC strategy maintains servo system stability while approaching the controlling target. Following the algorithm description, a proof of the controlling stability of ADRRAC strategy is presented in detail as well. Experiments on a variety of tracking tasks are conducted on a prototype of an EMA to investigate the working performance of the proposed control strategy. The experimental outcomes are reported, which verify the effectiveness of the ADRRAC strategy, compared to widely applied control strategies. According to the data analysis results, our controller is capable of obtaining an even faster system response, a higher tracking accuracy and a more stable system state.

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Disturbance-Improved Model-Free Adaptive Prediction Control for Discrete-Time Nonlinear Systems with Time Delay

Cited by 6 publications

References 20 publications

Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Difference Estimator Based Data-Driven Predictive Anti-Disturbance Control for Input-Delayed Nonlinear Systems with Unknown Dynamics

Design and Analysis of an Active Disturbance Rejection Robust Adaptive Control System for Electromechanical Actuator

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