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Design and evaluation of self-tuning optimal control for vibration suppression of magnetorheological semi-active suspension

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2025年第1期44卷 547-564页

作者： Li, Gang Gan, Yu Liu, Qianjie Xie, Miaojin Huang, Qingsheng Zeng, Liping East China Jiaotong Univ Key Lab Vehicle Intelligent Equipment & Control Na Nanchang 330013 Peoples R China East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330013 Peoples R China Yingtan Appl Engn Sch Yingtan Peoples R China

The objective of this paper is to enhance the vibration damping capabilities of magnetorheological (MR) semi-active suspension by utilizing a self-tuning LQG control method. Firstly, to determine the mechanical model of MR damper using the hyperbolic tangent model, mechanical experiments were performed with a damping force test machine under various input excitations. Experimental and simulation data were compared to confirm the accuracy of the mechanical model for MR damper. The MR damper is then incorporated into the vehicle's semi-active suspension system to create a quarter-car suspension model. In order to tackle the problems associated with low control accuracy and poor dynamic adjustment in traditional LQG controllers where the weighting matrix coefficients are difficult to determine, a self-tuning LQG controller based on gravity search algorithm (GSA-LQG) was designed. Finally, the designed controller performance was evaluated by the simulation and experimental results obtained from the random and sinusoidal excitation roads. Experimental data reveal that when subjected to GSA-LQG control, the suspension control performance is considerably enhanced compared to the passive control, PID control and conventional LQG control suspension. Based on these outcome, it can be concluded that the proposed algorithm is effective and the experimental platform is feasible.

关键词： Magnetorheological damper semi-active suspension LQG control gravitational search algorithm

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Probabilistic reliability assessment method for max ground settlement prediction of subway tunnel under uncertain construction information

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COMPUTERS AND GEOTECHNICS 2025年 177卷

作者： Chen, Yangyang Liu, Wen Ai, Demi Zhu, Hongping Du, Yanliang Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Control Struct Wuhan Peoples R China CCCC Wuhan ZhiXing Int Engn Consulting Co Ltd Wuhan Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen Peoples R China

Ground settlement resulting from shield tunnelling in densely populated areas has a significant impact on the surrounding environment, while accurate prediction of max ground settlement (MGS) is challenging under uncertain construction conditions. This paper investigates the vine copula probabilistic dependence approach for MGS predictions with incomplete information. A Monte Carlo simulation framework is established to incorporates vine copula analysis for eight identified soil parameters. Finite element (FE) method was used to model construction tunnels with different parameters and determine the MGS induced by excavation. The modelling results were used to construct six MGS base learners, which were created using six machine learning models combined with hybrid particle swarm optimisation (PSO) and gravity search algorithms (GSA). The integrated learning model combined six distinct base learners to generate a meta-learner. Improved hybrid GSA and PSO leveraged the global search capabilities of PSO and the local search abilities of GSA to optimize the integrated learning model. The FE model and meta-model predictions of MGS were validated using twelve uncertain input parameters. The results suggested that the hybrid GSA and PSO enhanced the precision of regression in the integrated learning model, and the resulting meta-model improved the reliability of MGS predictions in situations with uncertain information.

关键词： Multiple copula Integration learning model Particle swarm optimization gravitational search algorithm Max ground settlement Uncertain construction information

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Solution of unit commitment problem using gravitational search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 85-94页

作者： Roy, Provas Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

In this article, gravitational search algorithm (GSA) is proposed to solve thermal unit commitment (UC) problem. The objective of UC is to determine the optimal generation of the committed units to meet the load demand and spinning reserve at each time interval, such that the overall cost of generation is minimized, while satisfying different operational constraints. GSA is a new cooperative agents' approach, which is inspired by the observation of the behaviors of all the masses present in the universe due to gravitation force. The proposed method is implemented and tested using MATLAB programming. The tests are carried out using six systems having 10, 20, 40, 60,80 and 100 units during a scheduling period of 24 h. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, simulated annealing (SA), genetic algorithm (GA), evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO), improved PSO (IPSO), hybrid PSO (HPSO), binary coded PSO (BCPSO), quantum-inspired evolutionary algorithm (QEA), improved quantum-inspired evolutionary algorithm (IQEA), Muller method, quadratic model (QM), iterative linear algorithm (ILA) and binary real coded firefly algorithm (BRCFF). (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Unit commitment Generation scheduling Spinning reserve Ramp rate gravitational search algorithm Law of gravity

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Facing the classification of binary problems with a hybrid system based on quantum-inspired binary gravitational search algorithm and K-NN method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第10期26卷 2424-2430页

作者： Han, XiaoHong Quan, Long Xiong, XiaoYan Wu, Bing Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan 030024 Peoples R China

Since given classification data often contains redundant, useless or misleading features, feature selection is an important pre-processing step for solving classification problems. This problem is often solved by applying evolutionary algorithms to decrease the dimensional number of features involved. Removing irrelevant features in the feature space and identifying relevant features correctly is the primary objective, which can increase classification accuracy. In this paper, a novel QBGSA-K-NN hybrid system which hybridizes the quantum-inspired binary gravitational search algorithm (QBGSA) with the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) is proposed. The main aim of this system is to improve classification accuracy with an appropriate feature subset in binary problems. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The experimental results show that the proposed method is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Feature selection Binary classification gravitational search algorithm Binary gravitational search algorithm Quantum computing K-nearest neighbor

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Reliability improvement in distribution systems employing an integrated voltage sag mitigation method using binary gravitational search algorithm

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Journal of Central South University 2013年第11期20卷 3002-3014页

作者： Salman Nesrullah Mohamed Azah Shareef Hussain Department of Electrical Electronics and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia

A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM ***,an optimal DNR is applied to reduce the propagated voltage sags during the test *** second stage involves optimal placement of the DSTATCOM to assist the already reconfigured *** gravitational search algorithm is used in the process of optimal DNR and in placing *** assessment is performed using the well-known *** simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.

关键词： voltage sag reliability network reconfiguration DSTATCOM gravitational search algorithm

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Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

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KNOWLEDGE-BASED SYSTEMS 2013年 39卷 34-44页

作者： Zhang, Weiping Niu, Peifeng Li, Guoqiang Li, Pengfei Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Qinhuangdao Inst Technol Qinhuangdao 066100 Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Peoples R China

Accurate heat rate forecasting is very important in ensuring the economic, efficient, and safe operation of a steam turbine unit. The support vector machine (SVM) is a novel tool from the artificial intelligence field that has been successfully applied to heat rate forecasting. The least squares SVM (LS-SVM) is an improved algorithm based on the SVM. LS-SVM has minimal computational complexity and fast calculation. However, traditional LS-SVM, which was established by using offline data samples, can no longer accurately describe the actual system working condition, thereby resulting in problems when directly used in heat rate prediction. In this paper, a heat rate forecasting method based on online LS-SVM, which possesses dynamic prediction functions, is proposed. To avoid blindness and inaccuracy in parameter selection, the gravitational search algorithm (GSA) is used to optimize the regularization parameter gamma and the kernel parameter sigma(2) of the online LS-SVM modeling. The results confirm the efficiency of the proposed method. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Steam turbine Least squares support vector machine Online learning gravitational search algorithm Heat rate

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Multi-objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期44卷 282-292页

作者： Mondal, Soumitra Bhattacharya, Aniruddha Dey, Sunita Haider Nee Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 W Bengal India Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

In this paper an economic emission load dispatch (EELD) problem is solved to minimize the emission of nitrogen oxides (NOx) and fuel cost, considering both thermal generators and wind turbines. The effects of wind power on overall NOx emission are also investigated here. To find the optimum emission dispatch, optimum fuel cost, best compromising emission and fuel cost, a newly developed optimization technique, called gravitational search algorithm (GSA) has been applied. GSA is based on the Newton's law of gravity and mass interactions. In GSA, the searcher agents are collection of masses which interact with each other using laws of gravity and motion of Newton. IEEE 30-bus system having six conventional thermal generators has been considered as test system. Two extra wind turbines are also placed at two weak load bus of the system. Two Weak load buses have been selected based on their L-index value. After placing the wind power sources, those buses have been considered as generator bus. Minimum fuel cost, minimum emission and best compromising solution obtained by GSA are compared with those of biogeography-based optimization (BBO). The results show that the GSA surpasses the other available techniques in terms of solution quality and computational efficiency. (C) 2012 Published by Elsevier Ltd.

关键词： Emission dispatch Fuel cost gravitational search algorithm Law of gravity Law of motion Wind turbine

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Multi-distributed generation planning using hybrid particle swarm optimisation- gravitational search algorithm including voltage rise issue

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IET GENERATION TRANSMISSION & DISTRIBUTION 2013年第9期7卷 929-942页

作者： Tan, Wen Shan Hassan, Mohammad Yusri Rahman, Hasimah Abdul Abdullah, Md Pauzi Hussin, Faridah Univ Teknol Malaysia UTM Ctr Elect Energy Syst CEES Fac Elect Engn Johor Baharu 81310 Johor Malaysia

Distributed generation (DG) has been becoming more well-known in the power sector because of its ability in power loss reduction, low investment cost, increase reliability, and most significantly, to exploit renewable-energy resources. In this study, a multi-objective index-based approach for optimally determining the placement and size of multi-DG units in distribution systems, including the voltage rise phenomenon is proposed. The proposed approach considers a wide range of technical aspects such as the total real power losses in the system, the voltage profile, the mega volt ampere intake by the grid, the DG quantity and the greenhouse gases emission. A novel hybrid population-based algorithm with the combination of particle swarm optimisation (PSO), and gravitational search algorithm (GSA) is introduced. To reveal the validity of the hybrid PSO-GSA, an analysis is carried out on 69-bus systems then compared with results obtained by pre-combined methods. The outcomes verify that the proposed algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimisation problem.

关键词： air pollution control costing distributed power generation integer programming losses nonlinear programming particle swarm optimisation power distribution economics power distribution planning power distribution reliability power grids renewable energy sources search problems multidistributed generation planning hybrid particle swarm optimisation gravitational search algorithm DG power sector power loss reduction investment cost reliability renewable-energy resource multiobjective index-based approach voltage rise phenomenon power grid greenhouse gas emission hybrid population-based algorithm PSO GSA mixed integer nonlinear optimisation problem

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Inverse kinematics solution of demolition manipulator based on global mapping

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第5期238卷 1561-1572页

作者： Zong, Yimo Huang, Jianzhong Bao, Jiahan Cen, Yuwan Sun, Deshang Anhui Univ Technol Sch Mech Engn Maanshan Peoples R China Engn Technol Res Ctr Hydraul Vibrat Technol Maanshan Anhui Peoples R China Anhui Univ Technol Sch Mech Engn 59 Hudong Rd Maanshan 243000 Anhui Peoples R China

In solving the inverse kinematics of the redundant hydraulic manipulator of the demolition robot, there are problems of multiple solutions and the contradiction between the real-time and accuracy of the calculation. A series calculation strategy combining global mapping and local search was proposed. Firstly, the inverse kinematics solution was mapped from the global space to the local space of the working point by the global neural network model, and then the inverse solution results were obtained by the local search of the gravitational search algorithm. To avoid the GSA falling into the local minima and improve the search speed and accuracy, an improved gravitational particle adaptive step search strategy was proposed to balance the exploration and development stage of the population. The simulation and experimental results show that the global than local inverse solution algorithm can avoid the multi-solution problems and ensure the stability and accuracy of the solution on the basis of real-time inversion.

关键词： Redundant manipulator inverse kinematics neural network algorithm gravitational search algorithm

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Identification of Hammerstein-Wiener Systems with State-Space Subsystems Based on the Improved PSO and GSA algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第5期42卷 2755-2781页

作者： Zong, Tiancheng Li, Junhong Lu, Guoping Nantong Univ Sch Elect Engn Seyuan Rd Nantong 226019 Jiangsu Peoples R China Nantong Univ Sch Informat Sci & Technol Seyuan Rd Nantong 226019 Jiangsu Peoples R China

This paper investigates the parameter estimation of Hammerstein-Wiener systems whose linear subsystems are observable state-space models. The particle swarm optimization (PSO) and gravitational search (GSA) algorithms are combined to derive a novel method for identifying the discussed systems. To improve the optimization ability, some improvements are employed, including adding the oscillating exponential decay inertia weight and fuzzy membership in PSO (OFPSO) and introducing the chaotic optimization mechanism to GSA (CGSA). Thus, the combination of OFPSO and CGSA (OFPSO-CGSA) method is proposed. Its principle is to utilize coordinated behaviors of the speed in the OFPSO algorithm and the acceleration in the CGSA algorithm to update the particle position. Moreover, the convergence analysis proves that the OFPSO-CGSA algorithm converges to the global solution with probability one. Two simulations of the Hammerstein-Wiener nonlinear systems with two-dimensional and three-dimensional linear state space subsystems verify that the algorithms are effective, and OFPSO-CGSA has the best performance considering the convergence speed and identification accuracy in the algorithm comparison.

关键词： System identification Hammerstein-Wiener systems Linear state-space subsystem Particle swarm optimization gravitational search algorithm

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