scholarly journals Application of Support Vector Machine-Based Semiactive Control for Seismic Protection of Structures with Magnetorheological Dampers

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Chunxiang Li ◽  
Qing Liu ◽  
Shengning Lan
Keyword(s):  
Support Vector Machine ◽  
Control Strategy ◽  
Control Strategies ◽  
Seismic Protection ◽  
Support Vector ◽  
Semiactive Control ◽  
Mr Dampers ◽  
Acceleration Amplification ◽  
Switch Control ◽  
Lqr Controller

Based on recent research by Li and Liu in 2011, this paper proposes the application of support vector machine- (SVM-) based semiactive control methodology for seismic protection of structures with magnetorheological (MR) dampers. An important and challenging task of designing the MR dampers is to develop an effective semiactive control strategy that can fully exploit the capabilities of MR dampers. However, amplification of the local acceleration response of structures exists in the widely used semiactive control strategies, namely “Switch” control strategies. Then the SVM-based semiactive control strategy has been employed to design MR dampers. Firstly, the LQR controller for the numerical model of a multistory structure formulated using the dynamic dense method is constructed by using the classic LQR control theory. Secondly, an SVM model which comprises the observers and controllers in the control system is designed and trained to emulate the performance of the LQR controller. Finally, an online autofeedback semiactive control strategy is developed by resorting to SVM and then used for designing MR dampers. Simulation results show that the MR dampers utilizing the SVM-based semiactive control algorithm, which eliminates the local acceleration amplification phenomenon, can remarkably reduce the displacement, velocity, and acceleration responses of the structure.

scholarly journals Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 426 ◽  
Author(s):  
Yongliang Zheng ◽  
Feng He ◽  
Xinze Shen ◽  
Xuesheng Jiang
Keyword(s):  
Support Vector Machine ◽  
Fuel Cell ◽  
Fuzzy Control ◽  
Working Conditions ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Least Square ◽  
Support Vector ◽  
Fuzzy Control Strategy

Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.

Computer Simulating Effluent Quality of Vertical Tube Biological Reactor Using Support Vector Machine

2011 ◽  
Vol 219-220 ◽  
pp. 322-326
Author(s):  
Jing Zhang ◽  
Ji Ti Zhou ◽  
Yu Ming Li ◽  
Hua Yang
Keyword(s):  
Support Vector Machine ◽  
Control Strategies ◽  
Predictive Ability ◽  
Vertical Tube ◽  
Support Vector ◽  
Effluent Quality ◽  
Promising Tool ◽  
Treatment Processes ◽  
Advanced Control

Effluent quality of vertical tube biological reactor (VTBR) was simulated by support vector machine. AverageR2of the training set and the test set were used to balance the fitness and the predictive ability of SVR models, and evaluate the general performance of SVR models. Following a special grid search procedure, optimal parameters were determined for these SVR models. Results indicted that these SVR models have good fitness and predictive ability. The developed model may be integrated into an advanced control system for the VTBR using different control strategies with further work. Furthermore, SVR was found to be a useful and promising tool that is worth consideration in the prediction of effluent of wastewater treatment processes.

scholarly journals APPLICATION OF ADAPTIVE WAVELET NETWORKS FOR VIBRATION CONTROL OF BASE ISOLATED STRUCTURES

2010 ◽  
Vol 08 (05) ◽  
pp. 773-791 ◽  
Author(s):  
HAMID REZA KARIMI ◽  
MAURICIO ZAPATEIRO ◽  
NINGSU LUO
Keyword(s):  
Control Strategy ◽  
Supervisory Control ◽  
Feedback Linearization ◽  
Semiactive Control ◽  
Adaptive Wavelet ◽  
Wavelet Networks ◽  
Mr Dampers ◽  
Adaptive Laws ◽  
Adaptive Control Strategy ◽  
And Control

This paper presents an application of wavelet networks (WNs) in identification and control design for a class of structures equipped with a type of semiactive actuators, which are called magnetorheological (MR) dampers. The nonlinear model is identified based on a WN framework. Based on the technique of feedback linearization, supervisory control and H∞ control, an adaptive control strategy is developed to compensate for the nonlinearity in the structure so as to enhance the response of the system to earthquake type inputs. Furthermore, the parameter adaptive laws of the WN are developed. In particular, it is shown that the proposed control strategy offers a reasonably effective approach to semiactive control of structures. The applicability of the proposed method is illustrated on a building structure by computer simulation.

scholarly journals Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Ling Ai ◽  
Yang Xu ◽  
Liwei Deng ◽  
Kok Lay Teo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Predictive Control ◽  
Control Strategy ◽  
Distributed Parameter Systems ◽  
Diffusion Reaction ◽  
Support Vector ◽  
Distributed Parameter ◽  
Generalized Predictive Control ◽  
Parabolic Distributed Parameter Systems

This manuscript addresses a new multivariate generalized predictive control strategy using the least squares support vector machine for parabolic distributed parameter systems. First, a set of proper orthogonal decomposition-based spatial basis functions constructed from a carefully selected set of data is used in a Galerkin projection for the building of an approximate low-dimensional lumped parameter systems. Then, the temporal autoregressive exogenous model obtained by the least squares support vector machine is applied in the design of a multivariate generalized predictive control strategy. Finally, the effectiveness of the proposed multivariate generalized predictive control strategy is verified through a numerical simulation study on a typical diffusion-reaction process in radical symmetry.

Comparative Studies of Semiactive Control Strategies for MR Dampers: Pure Simulation and Real-Time Hybrid Tests

2013 ◽  
Vol 139 (7) ◽  
pp. 1237-1248 ◽  
Author(s):  
Young-Jin Cha ◽  
Jianqiu Zhang ◽  
Anil K. Agrawal ◽  
Baiping Dong ◽  
Anthony Friedman ◽  
...  
Keyword(s):  
Real Time ◽  
Comparative Studies ◽  
Control Strategies ◽  
Semiactive Control ◽  
Mr Dampers

scholarly journals Research on Vibration Reduction Control Based on Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Rongyao Yuan ◽  
Yang Yang ◽  
Chao Su ◽  
Shaopei Hu ◽  
Heng Zhang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Frame Structure ◽  
Semiactive Control ◽  
Damping Force ◽  
Mr Dampers ◽  
Damping Control ◽  
Optimal Damping ◽  
Optimal Value ◽  
Optimal Action

Magnetorheological (MR) dampers, as an intelligent vibration damping device, can quickly change the damping size of the material in milliseconds. The traditional semiactive control strategy cannot give full play to the ability of the MR dampers to consume energy and reduce vibration under different currents, and it is difficult to control the MR dampers accurately. In this paper, a semiactive control strategy based on reinforcement learning (RL) is proposed, which is based on “exploring” to learn the optimal value of the MR dampers at each step of the operation, the applied current value. During damping control, the learned optimal action value for each step is input into the MR dampers so that they provide the optimal damping force to the structure. Applying this strategy to a two-layer frame structure was found to provide more accurate control of the MR dampers, significantly improving the damping effect of the MR dampers.

Simulation on Fault Detection Optimization Model of Large Intelligent Electronic Inverter

2014 ◽  
Vol 986-987 ◽  
pp. 1587-1590
Author(s):  
Chun Ping Huang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Large Scale ◽  
Control Strategies ◽  
Support Vector ◽  
Current Voltage ◽  
Diagnosis Method ◽  
Engine Failure ◽  
Particle Swarm Method

When a fault of power system occurs in grid-connected point, a serious impact is added to electronic inverter in photovoltaic micro-network internal including current, voltage and phase, the appropriate control strategies for completing fault detection of large-scale intelligent electronic inverter, is one of the currently hotspot difficulties. An engine fault diagnosis method based on PSO-SVM is proposed. Particle swarm method is used to search engine fault signals in designated space, so as to obtain optimal particle and provide the basis for an engine fault diagnosis. Support vector machine (SVM) method is applied to classify engine failure signal to complete the engine fault diagnosis. Experimental results show that the proposed algorithm for the engine fault diagnosis, can greatly improve the accuracy of diagnosis, so as to meet the needs of actual production, life, and achieve satisfactory results.

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