Adaptive backstepping controller with extended state observer for load following of nuclear power plant

Hui, Jiuwu; Ling, Jun; Gu, Kai; Yuan, Jingqi

doi:10.1016/j.pnucene.2021.103745

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…Norouzi et al (Norouzi et al 2013) introduced a Parallel Integer Coded Genetic Algorithm to obtain the best configuration for core optimization. Hui et al (Hui et al 2021) developed an adaptive backstepping control strategy with an extended state observer for the load following of NPPs.…”

Section: System Structure Component Operation and Control Optimizationmentioning

confidence: 99%

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Bao

Zhang

et al. 2022

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This report presents the project Idaho National Laboratory conducted for the Nuclear Regulatory Commission (NRC) to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. The report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers.The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations, which was published by the NRC as in the Federal Register Notice NRC-2021-0048 in April 2021. A summary of the survey, including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey, is provided in the report.Finally, the report investigates potential applications of using AI/ML in operating nuclear power plants and advanced reactors (both advanced light-water reactors and advanced non-light-water reactors) to improve nuclear plant safety and efficiency. Three main application fields are considered: plant safety and security assessments; plant degradation modeling, fault and accident diagnosis and prognosis; and plant operation and maintenance efficiency improvement. v TABLE OF CONTENTS ABSTRACT .

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Section: System Structure Component Operation and Control Optimizationmentioning

confidence: 99%

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Bao

Zhang

et al. 2022

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“…This filter may effectively eliminate the effect raised by the boundary layer error at each step. In [146] authors proposed a specific control parameter that is based on the fuzzy rules. This control uses only one fuzzy system in each subsystem to approximate the unknown control gain and the unknown nonlinear function, as well as the differential of the former subsystem's virtual control.…”

Section: Review Of Backstepping Controlmentioning

confidence: 99%

A Review on Popular Control Applications in Wind Energy Conversion System Based on Permanent Magnet Generator PMSG

et al. 2022

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There has always been a high expectation that wind generation systems would capture maximum power and integrate properly with the grid. Utilizing a wind generation system with increased management to meet the growing electricity demand is a clever way of accomplishing this. However, wind power generation systems require a sophisticated, unique, and dependable control mechanism in order to achieve stability and efficiency. To improve the operation of the wind energy conversion method, researchers are continually addressing the obstacles that presently exist. Therefore, it is necessary to know which control can improve the whole system’s performance and ensure its successful integration into the network, despite the variable conductions. This article examines wind turbine control system techniques and controller trends related to the permanent magnet synchronous generator. It presents an overview of the most popular control strategies that have been used to control the PMSG wind power conversion system. Among others, we mention nonlinear sliding mode, direct power, backstepping and predictive currents control. First, a description of each control is presented, followed by a simulation performed in the Matlab/Simulink environment to evaluate the performance of each control in terms of reference tracking, response time, stability and the quality of the signal delivered to the network under variable wind conditions. Finally, to get a clear idea of the effect of each control, this work was concluded with a comparative study of the four controls.

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“…Quadrotor has got considerable attention for its advantages in structural simplicity, long endurance, operational convenience, and agility. Many control methods have been given rise to address the stabilization and tracking problems of quadrotors like model predictive control, 1,2 backstepping, [3][4][5][6][7][8][9][10] finite-time control, [11][12][13] sliding mode control 14,15 and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Backstepping [3][4][5][6][7][8][9][10] is a useful design technique for addressing stabilization and tracking control problems, it decomposes the system into several subsystems for which the Lyapunov function and the virtual controller are designed until the whole system, and finally, the subsystems are integrated to realize the design of the whole control law. Using the backstepping technique, a fault-tolerant tracking control problem 4 was addressed for ocean surface vessels.…”

Section: Introductionmentioning

confidence: 99%

“…The paper 5 constructed an improved adaptive tracking controller with backstepping such that the closed‐loop signal is globally bounded. An adaptive backstepping control method 6 was proposed to address the load‐following problem of nuclear power plants. By using reinforcement learning and backstepping, the optimized tracking controller 7 was designed for quadrotors.…”

Section: Introductionmentioning

confidence: 99%

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Geometric prescribed‐time control of quadrotors with adaptive extended state observers

Wang,

Pei

2024

Intl J Robust & Nonlinear

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This article studies an adaptive extended state observer (AESO) based prescribed‐time geometric attitude stabilization problem of quadrotors with endogenous uncertainties, exogenous disturbances, and actuator saturation, which can be estimated and compensated by AESOs. The employment of the geometric representation method for system attitude can effectively avoid ambiguity or singularity problems that may arise with other representations. The temporal and scale transformations are employed to solve the global boundedness problem on infinite time instead of the prescribed‐time convergence problem on finite time. By integrating the proposed controller with the backstepping technique, the system of quadrotors can be prescribed‐time stable. Further, a prescribed‐time geometric attitude control technique is devised such that the control signal is bounded and the state signals converge to a small domain near the equilibrium point in the prescribed time. The prescribed time is determined by the user and is independent of the initial conditions. The comparative simulations with different initial information are performed to demonstrate the effectiveness of the proposed design technique.

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Adaptive backstepping controller with extended state observer for load following of nuclear power plant

Cited by 15 publications

References 21 publications

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

A Review on Popular Control Applications in Wind Energy Conversion System Based on Permanent Magnet Generator PMSG

Geometric prescribed‐time control of quadrotors with adaptive extended state observers

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