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Active Disturbance Rejection Based Adaptive Dynamic Surface Control for Nonlinear Systems

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 8607-8617页

作者： Zhou, Lan Sun, Yongbo He, Yong Li, Hongyi Zhang, Xian-Ming Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Sch Mech Engn Xiangtan 411201 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia

This paper presents an adaptive dynamic surface control (DSC) method for nonlinear systems with multiple disturbances, parameter uncertainties, and unknown nonlinear dynamics, using a reduced-order extended state observer (ROESO). Compared to related methods, this proposed approach offers several distinct advantages: (i) The method does not require knowledge of the upper bound function for unknown nonlinear dynamics, allowing the controlled plant to not be bounded-input bounded-state, thus expanding the applicability of the DSC method;(ii) By utilizing known model information, an ROESO is designed to handle mismatched uncertainties and disturbances, reducing the load on the observer and enhancing its capability to suppress unknown nonlinear dynamics;(iii) This method employs an adaptive output-feedback DSC approach, which is more practical and easier to implement than state-feedback methods;and (iv) The observer gain, adaptive gain, and DSC gain are simultaneously optimized using a particle swarm optimization algorithm. Additionally, detailed stability analysis is provided, and simulations and comparative experiments are conducted on a rotational system to demonstrate the efficacy and superiority of the proposed method.

关键词： Uncertainty Nonlinear dynamical systems Backstepping Observers Heuristic algorithms Adaptive control Complexity theory Attenuation Adaptation models Uncertain systems Mismatched disturbances dynamic surface control reduced-order extended state observer particle swarm optimization algorithm

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Thermodynamic performance prediction and optimization of a 1 kW ocean thermal energy cogeneration system based on artificial neural network

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ENERGY 2025年 314卷

作者： Zhang, Yuan Li, Yifan Tian, Zhen Yang, Chao Peng, Hao Kan, Ankang Gao, Wenzhong Shanghai Maritime Univ Coll Merchant Marine Shanghai 201306 Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China

In this paper, a prediction model based on the artificial neural network (ANN) method was constructed, and the thermodynamic performance prediction and optimization were conducted for a system for ocean thermal energy conversion that integrates an air conditioning cycle (OTEC-AC). By employing the variable importance measure (VIM) method, ten key parameters were selected as input parameters, and three output parameters, including system power generation (Wele), cold energy efficiency (eta co), and energy efficiency (eta en) were defined. The accuracies of prediction models constructed by the back propagation neural network (BPNN) and BPNN optimized by the particle swarm optimization algorithm (BPNN-PSO) were compared. Analysis of the thermodynamic characteristics and multi-objective optimization of the system were performed, and performance differences between the experimental data-based and the BPNN-PSO model-based system were compared. Results showed that optimizing the initial weights and thresholds of the model using the PSO algorithm could enhance the model's generalization capability. Multi-objective optimal results based on prediction data were very close to those based on experimental data, with a maximum relative error of 4.4498 %. This study validates the feasibility of using the ANN model for data prediction in OTEC cogeneration systems, which could provide guidelines for the optimal design of related systems.

关键词： OTEC Air conditioning Artificial neural network particle swarm optimization algorithm Multi-objective optimization

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Soil moisture retrieval using GNSS signal-to-noise ratio data based on an improved optimal arc selection method

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GPS SOLUTIONS 2025年第1期29卷 1-14页

作者： He, Jiaxing Zheng, Nanshan Ding, Rui Liu, Xuexi China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Peoples R China China Univ Min & Technol Key Lab Land Environm & Disaster Monitoring Minist Nat Resources Xuzhou 221116 Peoples R China

Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) can be used to monitor soil moisture by establishing a relationship between phase and soil moisture. Therefore, the accuracy of the phase value is very important. However, topography and vegetation can introduce errors in the phase values when processing the raw signal-to-noise ratio reflection component (SRC). This study proposes an optimal arc selection (OAS) method to overcome this limitation. The novelty of this method is the use of entropy to evaluate the accuracy of curve fitting and the use of a particle swarm optimization algorithm to search for the optimal elevation range of SRC. We processed SNR data from 3 GNSS stations and provided the verification results through in-situ soil moisture measurements. The results showed that the phase values calculated using the OAS method were more accurate than those calculated using the conventional method. The new method improved the agreement between GNSS-derived soil moisture and in-situ measurements, with a reduction of 29% in root mean square error and 31% in mean absolute error. This suggests that the OAS method can improve the capacity of soil moisture retrieval in undulating terrain areas and promote the development of GNSS-IR.

关键词： GNSS-IR Soil moisture particle swarm optimization algorithm Entropy method

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Design of backstepping control for nonlinear systems based on command filter and uncertainty estimation

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ASIAN JOURNAL OF CONTROL 2025年第0期

作者： Sun, Yongbo Zhou, Lan Jiang, Fuxi Li, Meiliu Xiao, Wenbin Hunan Univ Sci & Technol Sch Mech Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan Peoples R China

This paper presents an improved command-filtered backstepping control strategy for a class of multisource-disturbed nonlinear systems based on an active-uncertainty-estimation-and-compensation approach. Exploiting the available model information of a plant, a reduced-order extended-state observer is designed to estimate a matched total uncertainty. Command filters are constructed to estimate virtual control inputs. Both the estimated states and estimation errors are used for the construction of the backstepping control law, which guarantees the stability of the system and compensates for uncertainties. Moreover, a particle swarm optimization algorithm is adopted to simultaneously optimize the state observer gain and backstepping control gains. Finally, a case study on a rotational system shows that the presented method achieves better uncertainty suppression and tracking performance in both transient and steady state than other related methods.

关键词： backstepping control extended state observer particle swarm optimization algorithm tracking control uncertainty compensation

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Prediction of rotary seal leakage rate under the influence of stress relaxation based on G A-PSO-BP hybrid algorithm

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷 015581-015581页

作者： Zhang, Yi He, Wei Hu, Ling Lou, Zicheng Zhang, Daqin Wang, Zheng Southwest Petr Univ Sch Mechatron Engn Xindu Ave 8 Chengdu 610500 Sichuan Peoples R China

Rotary combination seals of composite impactors are susceptible to failure and relaxation in high-pressure, high-temperature deep-well environments. The aim of this study is to analyze the effect of stress relaxation on the performance of the seals and to assess their performance under different operating conditions. The study adopts GA-PSO-BP algorithm to predict the leakage, simulates the contact pressure change of the sealing surface before and after stress relaxation by Abqus software, and calculates the leakage rate by using MATLAB software in combination with the thermoelastic flow lubrication model and the finite element analysis data, to establish the prediction model dataset. The study also optimised the neural network prediction model and verified the accuracy of the model experimentally. The results show that increasing pressure leads to an increase in contact pressure, but relaxation increases contact length and decreases maximum pressure, and increasing compression increases and then decreases contact pressure and increases the peak value, but relaxation decreases the peak value and increases the risk of leakage. The increase in speed and temperature both lead to an increase in leakage rate, and the GA-PSO-BP model predicts more accurately the rotary combination seals with a higher leakage rate after considering the stress relaxation.

关键词： rotary combination seal stress relaxation BP neural network genetic algorithm particle swarm optimization algorithm leakage

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A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

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ENERGIES 2025年第5期18卷 1136-1136页

作者： Du, Jie Chen, Shuaizhi Pan, Linlin Liu, Yubao Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Precis Reg Earth Modeling & Informat Ctr Nanjing 210044 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Nanjing Univ Informat Sci & Technol Sch Atmospher Phys Nanjing 210044 Peoples R China

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

关键词： wind speed predicting signal decomposition hybrid deep learning model particle swarm optimization algorithm

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optimization algorithm-Based Fault-Tolerant Resilient Control for Helicopter System: The Finite-Time Case

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JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS 2025年第2期20卷 021007页

作者： Satheesh, T. Anisha, T. M. Sakthivel, R. Elayabharath, V. T. Ren, Y. Karpagam Acad Higher Educ Dept Math Coimbatore 641021 India Bharathiar Univ Dept Appl Math Coimbatore 641046 India Sungkyunkwan Univ Dept Math Suwon 440746 South Korea Anhui Normal Univ Sch Math & Stat Wuhu 241000 Anhui Peoples R China

This paper centers on the finite-time control problem of the helicopter system in the midst of external disturbances, gain fluctuations, and actuator faults. Precisely, the finite-time boundedness and input-output finite-time stability are obtained concurrently to preclude huge undesirable values for both state and output, respectively, during specific transients. In particular, a particle swarm optimization setup is put forward for lowering the cost function and enhancing the overall reliability of the system. Furthermore, the fault-tolerant resilient control scheme is tailored to exhibit strong resistance to failures in the actuator and variations in the gain matrix. By blending the Lyapunov stability theory with the concept of finite time, we attain the essential requirements to ensure the stability of the closed-loop system over a finite span of time. Subsequently, the explicit methodology for obtaining the gain matrix is laid out pursuant to the established requirements. In particular, simulation results are presented to analyze the potential and significance of the devised control scheme.

关键词： helicopter system fault-tolerant resilient control finite-time theory particle swarm optimization algorithm

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An effective optimization algorithm for hydrogen fuel cell-based hybrid energy system: A sustainable microgrid approach

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 1341-1355页

作者： Abu, Sayem M. Hannan, M. A. Rahman, S. A. Long, Chua Yaw Ker, Pin Jern Wong, Richard T. K. Jang, Gilsoo Univ Tenaga Nas Dept Mech Engn COE Kajang 43000 Malaysia Sunway Univ Sch Engn & Technol Bandar Sunway 47500 Malaysia Korea Univ Sch Elect Engn Seoul 136701 South Korea Univ Tenaga Nas Dept Elect & Elect Engn COE Kajang 43000 Malaysia

--This paper presents the optimization of the performance of hybrid energy system (HES) integrating PV systems, hydrogen fuel cells, batteries, and supercapacitors for improved energy management and reduced hydrogen consumption in microgrid applications. The optimization is achieved using a particle swarm optimization (PSO) algorithm to adjust the proportional-integral controller. Key performance metrics, such as fuel flow rate, hydrogen consumption, and system stability, are targeted. Comparative analysis shows hydrogen consumption reduced by 18.35%, fuel flow rate by 19.87%, and oxygen utilization improved by 1.04%, resulting in better fuel cell efficiency. PV power consumption decreased by 17.53%, and battery and supercapacitor performance improved by 9.71% and 32%, respectively. The PSO-optimized system also reduces grid dependence by 2.21% and enhances stability by 7.58%. These results demonstrate the critical role of the fuel cell in the HES and highlight how PSO can advance sustainable energy systems by improving microgrid efficiency and reducing grid reliance.

关键词： Hybrid energy system Hydrogen fuel cell battery Supercapacitor particle swarm optimization algorithm

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Seismic analysis of coastal slope stability considering strength nonlinearity and tension cut-off

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MARINE GEORESOURCES & GEOTECHNOLOGY 2025年第4期43卷 586-606页

作者： Hu, Shihong Zhao, Lianheng Li, Liang Zuo, Shi Huang, Dongliang Ni, Xueqian Cent South Univ Sch Civil Engn Changsha Hunan Peoples R China Cent South Univ Key Lab Heavy haul Railway Engn Struct Minist Educ Changsha 410075 Hunan Peoples R China Hunan Prov Key Lab Disaster Prevent Mitigat Rail Transit Engn Struct Changsha 410075 Hunan Peoples R China Changsha Univ Coll Civil Engn Changsha 410022 Hunan Peoples R China

Tensile stresses may lead to cracks forming at the crest of coastal slopes and subsequent slope failure, particularly under seismic load conditions. Traditional shear strength failure criteria often overestimate the tensile strength of geomaterials, and in turn the factor of safety against sliding. This paper presents an improved nonlinear analysis method for assessing the stability of slopes, while considering tension cut-off in the failure criterion as well as the detrimental effects of inertial seismic loading and pore water pressure. The method employs the principle of energy consumption to obtain solutions for the geometry of the potential sliding surface and stress distribution, while the critical slope height and the stability factor are determined by means of a particle swarm optimization algorithm. Comparison with existing methods is conducted to validate the effectiveness and accuracy of this method. Results showed that the tensile strength of geomaterials had a significant impact on the stability of the slope and its associated failure mechanism, particularly for the vertical slope. This study could avoid the assumptions of the dilatancy angle on the velocity discontinuity and critical slope sliding surface, and reflect the tensile and nonlinear characteristics of the geomaterials well.

关键词： Coastal slope stability nonlinear properties tensile strength seismic load pore water pressure energy consumption analysis particle swarm optimization algorithm

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Virtually constrained generalized relative motion modeling and a control parameter optimizer for automatic carrier landing

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2024年第3期96卷 448-457页

作者： Zhang, Yiwei Li, Daochun Kan, Zi Yao, Zhuoer Xiang, Jinwu Beihang Univ Sch Aeronaut Sci & Engn Beijing Peoples R China Tianmushan Lab Hangzhou Zhejiang Peoples R China

PurposeThis paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is ***/methodology/approachA novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control *** control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea ***/valueThe proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

关键词： Automatic carrier landing system Virtually constrained generalized relative motion particle swarm optimization algorithm Control parameter optimization

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