Quantitative Tuning of Active Disturbance Rejection Controller for FOPTD Model With Application to Power Plant Control

Sun, Li; Xue, Wenchao; Li, Donghai; Zhu, Hongxia; Su, Zhi‐gang

doi:10.1109/tie.2021.3050372

Cited by 79 publications

(30 citation statements)

References 38 publications

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“…Since the only way to compensate the NMP plant as the cascaded integrators is to cancel the RHP zeros by identical unstable poles, which is not allowed. 34 It should be pointed out that the classical ESO is more or less model-free for most MP systems but not for the NMP systems with positive zeros. With the bandwidth limited by the RHP zeros, the classical ESO alone may be inadequate in obtaining the desired results.…”

Section: 22mentioning

confidence: 99%

“…Theorem 4 (Consistency and Stability 46 ). Considering the system (34) with Assumptions 7-10, and the ESKF with k satisfies inf k≥0 k ≜ > 0 and sup k≥0 k ≜ < ∞, there exists a positive definite matrix P such that,…”

Section: Consistency Stability and Optimalitymentioning

confidence: 99%

“…Theorem 5 (Asymptotic Optimality 46 ). Consider the system (34) with Assumptions 7-10 . If lim k→∞ Q k = 0, then the ESKF with parameter sequence k satisfies lim k→∞ k = 0 and lim k→∞ 1 k Q k = 0 is an asymptotic minimum variance estimator.…”

Section: Consistency Stability and Optimalitymentioning

confidence: 99%

“…[30][31][32] Considering NMP systems, several novel ESOs were proposed. 33,34 Reduced-order ESO was proposed and the convergency of the estimation error was proved. 12,35 Also, a new ESO design was carried out to deal with systems with multiple subsystems coupled where the model information of each subsystem was incorporated into the canonical model.…”

Section: Introductionmentioning

confidence: 99%

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An overview on recent progress of extended state observers for uncertain systems: Methods, theory, and applications

2021

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Extended state observer (ESO) is featured with online estimating both the system's states and the total effect of external disturbance as well as nonlinear uncertain dynamics. The original ESO design, proposed for the cascade-of-integrator system without measurement noise, has been well studied with theoretical foundation and many successful applications. In the last years, multifarious modified ESOs have also been developed to handle many engineering systems captured by the models with general nonlinear structure, time-delay, the noises with stochastic properties, and etc. This paper aims to comprehensively investigate the representative modified ESOs in recent years. Firstly, the ESO's design and tuning law are illustrated for general uncertain systems with multiple control inputs, multiple measured outputs and various uncertainties. The quantitative results regarding the estimation errors of ESO are also presented. Next, the extended state-based Kalman-typed filters to handle state estimation problem against both stochastic noises and uncertain dynamics are introduced. The consistency, stability, and asymptotic optimization of the proposed filters are rigorously shown. Furthermore, some novel designs of ESO to deal with the estimation problem of uncertain nonlinear systems with time-delay and measurement biases are discussed. We believe this overview will be helpful for practitioners in applications of recent modified ESOs.

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Section: 22mentioning

confidence: 99%

Section: Consistency Stability and Optimalitymentioning

confidence: 99%

Section: Consistency Stability and Optimalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An overview on recent progress of extended state observers for uncertain systems: Methods, theory, and applications

2021

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“…Solar energy is considered a promising power generation candidate [1] for sustainable development and is playing an increasingly important role in response to climate change [2] because of the heavy carbon emission in the conventional power plant [3]. It is applied in distributed and grid-connected systems [4] to power household appliances, commercial, and industrial equipment [5].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

Wang

Bao²,

Hua

et al. 2021

Sustainability

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Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

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Tracking control design for cyber‐physical systems with disturbances and input delays: An interval type‐2 fuzzy approach

Sakthivel,

Anusuya,

Kong

et al. 2024

Adaptive Control & Signal

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SummaryThe underlying intention of this work is to devise a tracking control protocol for the nonlinear cyber‐physical systems that are prone to external disturbances, time‐varying input delays and deception attacks. To be more precise, the modified repetitive tracking controller is formulated for ensuring the asymptotic tracking outcomes of the assayed system with the aim of addressing the impacts caused by external disturbances as well as input time‐varying delays. First of all, the nonlinearities of the investigated cyber‐physical systems are efficaciously approximated by the interval type‐2 fuzzy model approach. Further, the predictor technique is utilized in this study to mitigate the influence of input delays and specifically, the extended Smith predictor approach is employed. That is, the transfer function is inserted into the conventional Smith predictor's main feedback channel. In parallel, the active disturbance rejection technique is executed to reject and estimate the external disturbance impacts on the assayed system. Notably, the improved equivalent‐input‐disturbance estimator approach is tied up and more precisely, the estimated disturbances obtained from the afore‐said estimator are incorporated into the proposed controller. Whilst the deception attacks are characterized as a stochastic distributed random variable that are governed by the Bernoulli distribution. With all of this as a backdrop, the appropriate delay‐dependent constraints are expressed in the context of linear matrix inequalities by employing the Lyapunov stability theory. In line with the predetermined criteria, the relations for computing the required gain matrices are also formulated. Ultimately, the simulation analysis is carried out using two distinct numerical examples to confirm the importance of the proposed control mechanism from the perspective of theory as well as practice.

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Quantitative Tuning of Active Disturbance Rejection Controller for FOPTD Model With Application to Power Plant Control

Cited by 79 publications

References 38 publications

An overview on recent progress of extended state observers for uncertain systems: Methods, theory, and applications

An overview on recent progress of extended state observers for uncertain systems: Methods, theory, and applications

Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

Tracking control design for cyber‐physical systems with disturbances and input delays: An interval type‐2 fuzzy approach

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