In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of ...In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.展开更多
Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip＇ s flatness quality, the most frequently used methodology is to employ the closed-loop automati...Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip＇ s flatness quality, the most frequently used methodology is to employ the closed-loop automatic shape control system. However, in the shape control system, the shape-meter is always installed at the down way of the exit of the cold rolling mill and can not sense the changes of the strip flatness in the rolling gap directly. This kind of installation results in the delay of the feedback in the control system. Therefore, the stability and response performance of the system are strongly affected by the delay. At present, there is still no mature way to design controllers for systems with time delay. Although the conventional PID controller used in most practical applications has the capability to compensate the delay, the effect of the compensation is limited, especially for the systems with long time delay. Smith predictor, as a compensator for solving this problem, is now widely used in industry systems. However, the request of highly precise model of the system and the poor adaptive performance to the changes of related parameters limit the application of the Smith predictor in practice. In order to overcome the drawbacks of the Smith predictor, a new Smith predictor based on single neural network PID （SNN-PID） is proposed. Because the single neural network is employed into the Smith predictor to improve the controller＇s self-adaptability, the adaptive capability to the varying parameters of the system is improved. Meanwhile, for the purpose of solving the problems such as time-consuming and complicated calculation of the neural networks in real time, the learning coefficient of neural network is divided into several stages as usually done in expert control system. Therefore, the control system can obtain fast response due to the improved calculation speed of the neural networks. In order to validate the performance of the proposed controller, the experiment is conducted on the shape control system in a 300 mm four-high reversing cold rolling mill. The experimental results show that the SNN-PID with Smith predictor controller can effectively compensate the delay effects and achieve better control performance than the conventional PID controller.展开更多
Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbi...Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine(MCT)system,the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’uncertainties.This paper proposes an adaptive single neural control(ASNC)strategy for variable step-size perturb and observe(P&O)maximum power point tracking(MPPT)control.Firstly,to automatically update the neuron weights of SNC for the nonlinear systems,an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients.Secondly,aiming to generate the exact reference speed for ASNC to extract the maximum power,a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT.The robust stability of the MCT control system is guaranteed by the Lyapunov theorem.Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions,and the MCT system operates at maximum power point steadily.展开更多
The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrati...The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.展开更多
A new hydraulic actuator-hydraulic muscle （HM） is described, and the actuator＇s features and applications are analyzed, then a position servocontrol system in which HM is main actuator is set up. The mathematical m...A new hydraulic actuator-hydraulic muscle （HM） is described, and the actuator＇s features and applications are analyzed, then a position servocontrol system in which HM is main actuator is set up. The mathematical model of the system is built up and several control strategies are discussed. Based on the mathematical model, simulation research and experimental investigation with subsection PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control adopted respectively are carried out, and the results indicate that compared with PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control don＇t need controlled system＇s accurate model and have fast response, high control accuracy and strong robustness, they are very suitable for HM position servo control system.展开更多
Two common polymorphisms of the peroxisome proliferator-activated receptor gamma(PPARG) gene, rs1801282 and rs3856806, may be important candidate gene loci affecting the susceptibility to ischemic stroke. This case-co...Two common polymorphisms of the peroxisome proliferator-activated receptor gamma(PPARG) gene, rs1801282 and rs3856806, may be important candidate gene loci affecting the susceptibility to ischemic stroke. This case-control study sought to identify the relationship between these two single-nucleotide polymorphisms and ischemic stroke risk in a northern Chinese Han population. A total of 910 ischemic stroke participants were recruited from the First Hospital of China Medical University, Shenyang, China as a case group, of whom 895 completed the study. The 883 healthy controls were recruited from the Health Check Center of the First Hospital of China Medical University, Shenyang, China. All participants or family members provided informed consent. The study protocol was approved by the Ethics Committee of the First Hospital of China Medical University, China on February 20, 2012(approval No. 2012-38-1). The protocol was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR-COC-17013559). Plasma genomic DNA was extracted from all participants and analyzed for rs1801282 and rs3856806 single nucleotide polymorphisms using a SNaPshot Multiplex sequencing assay. Odds ratios(ORs) and 95% confidence intervals(CIs) were calculated using unconditional logistic regression to estimate the association between ischemic stroke and a particular genotype. Results demonstrated that the G allele frequency of the PPARG gene rs1801282 locus was significantly higher in the case group than in the control group(P < 0.001). Individuals carrying the G allele had a 1.844 fold increased risk of ischemic stroke(OR = 1.844, 95% CI: 1.286–2.645, P < 0.001). Individuals carrying the rs3856806 T allele had a 1.366 fold increased risk of ischemic stroke(OR = 1.366, 95% CI: 1.077–1.733, P = 0.010). The distribution frequencies of the PPARG gene haplotypes rs1801282-rs3856806 in the control and case groups were determined. The frequency of distribution in the G-T haplotype case group was significantly higher than that in the control group. The risk of ischemic stroke increased to 2.953 times in individuals carrying the G-T haplotype(OR = 2.953, 95% CI: 2.082–4.190, P < 0.001). The rs1801282 G allele and rs3856806 T allele had a multiplicative interaction(OR = 3.404, 95% CI: 1.631–7.102, P < 0.001) and additive interaction(RERI = 41.705, 95% CI: 14.586–68.824, AP = 0.860;95% CI: 0.779–0.940;S = 8.170, 95% CI: 3.772–17.697) on ischemic stroke risk, showing a synergistic effect. Of all ischemic stroke cases, 86% were attributed to the interaction of the G allele of rs1801282 and the T allele of rs3856806. The effect of the PPARG rs1801282 G allele on ischemic stroke risk was enhanced in the presence of the rs3856806 T allele(OR = 8.001 vs. 1.844). The effect of the rs3856806 T allele on ischemic stroke risk was also enhanced in the presence of the rs1801282 G allele(OR = 2.546 vs. 1.366). Our results confirmed that the G allele of the PPARG gene rs1801282 locus and the T allele of the rs3856806 locus may be independent risk factors for ischemic stroke in the Han population of northern China, with a synergistic effect between the two alleles.展开更多
Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal leve...Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.展开更多
Introduces the ceramic stove controlling system controlled by MSP430 single-chip computer. The system ameliorate the PID control method, adopts CHAOS-RBF, improves the accuracy of temperature control largely. There ar...Introduces the ceramic stove controlling system controlled by MSP430 single-chip computer. The system ameliorate the PID control method, adopts CHAOS-RBF, improves the accuracy of temperature control largely. There are two parts in this system, the lower machine measures the data and the upper machine with responsibility for data processing, displaying data and so on. Mean-while, using the serial communication RS-485 to realize the control of principal and subordinate station.展开更多
文摘In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.
基金supported by National Natural Science Foundation of China (Grant No. 604740044)Hebei Provincial Natural Science Foundation of China (Grant No. E2004000221)
文摘Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip＇ s flatness quality, the most frequently used methodology is to employ the closed-loop automatic shape control system. However, in the shape control system, the shape-meter is always installed at the down way of the exit of the cold rolling mill and can not sense the changes of the strip flatness in the rolling gap directly. This kind of installation results in the delay of the feedback in the control system. Therefore, the stability and response performance of the system are strongly affected by the delay. At present, there is still no mature way to design controllers for systems with time delay. Although the conventional PID controller used in most practical applications has the capability to compensate the delay, the effect of the compensation is limited, especially for the systems with long time delay. Smith predictor, as a compensator for solving this problem, is now widely used in industry systems. However, the request of highly precise model of the system and the poor adaptive performance to the changes of related parameters limit the application of the Smith predictor in practice. In order to overcome the drawbacks of the Smith predictor, a new Smith predictor based on single neural network PID （SNN-PID） is proposed. Because the single neural network is employed into the Smith predictor to improve the controller＇s self-adaptability, the adaptive capability to the varying parameters of the system is improved. Meanwhile, for the purpose of solving the problems such as time-consuming and complicated calculation of the neural networks in real time, the learning coefficient of neural network is divided into several stages as usually done in expert control system. Therefore, the control system can obtain fast response due to the improved calculation speed of the neural networks. In order to validate the performance of the proposed controller, the experiment is conducted on the shape control system in a 300 mm four-high reversing cold rolling mill. The experimental results show that the SNN-PID with Smith predictor controller can effectively compensate the delay effects and achieve better control performance than the conventional PID controller.
基金financially supported by the National Natural Science Foundation of China(Grant No.61673260)。
文摘Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine(MCT)system,the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’uncertainties.This paper proposes an adaptive single neural control(ASNC)strategy for variable step-size perturb and observe(P&O)maximum power point tracking(MPPT)control.Firstly,to automatically update the neuron weights of SNC for the nonlinear systems,an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients.Secondly,aiming to generate the exact reference speed for ASNC to extract the maximum power,a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT.The robust stability of the MCT control system is guaranteed by the Lyapunov theorem.Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions,and the MCT system operates at maximum power point steadily.
文摘The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.
文摘A new hydraulic actuator-hydraulic muscle （HM） is described, and the actuator＇s features and applications are analyzed, then a position servocontrol system in which HM is main actuator is set up. The mathematical model of the system is built up and several control strategies are discussed. Based on the mathematical model, simulation research and experimental investigation with subsection PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control adopted respectively are carried out, and the results indicate that compared with PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control don＇t need controlled system＇s accurate model and have fast response, high control accuracy and strong robustness, they are very suitable for HM position servo control system.
基金supported by the National Natural Science Foundation of China,No.81070913(to ZYH)
文摘Two common polymorphisms of the peroxisome proliferator-activated receptor gamma(PPARG) gene, rs1801282 and rs3856806, may be important candidate gene loci affecting the susceptibility to ischemic stroke. This case-control study sought to identify the relationship between these two single-nucleotide polymorphisms and ischemic stroke risk in a northern Chinese Han population. A total of 910 ischemic stroke participants were recruited from the First Hospital of China Medical University, Shenyang, China as a case group, of whom 895 completed the study. The 883 healthy controls were recruited from the Health Check Center of the First Hospital of China Medical University, Shenyang, China. All participants or family members provided informed consent. The study protocol was approved by the Ethics Committee of the First Hospital of China Medical University, China on February 20, 2012(approval No. 2012-38-1). The protocol was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR-COC-17013559). Plasma genomic DNA was extracted from all participants and analyzed for rs1801282 and rs3856806 single nucleotide polymorphisms using a SNaPshot Multiplex sequencing assay. Odds ratios(ORs) and 95% confidence intervals(CIs) were calculated using unconditional logistic regression to estimate the association between ischemic stroke and a particular genotype. Results demonstrated that the G allele frequency of the PPARG gene rs1801282 locus was significantly higher in the case group than in the control group(P < 0.001). Individuals carrying the G allele had a 1.844 fold increased risk of ischemic stroke(OR = 1.844, 95% CI: 1.286–2.645, P < 0.001). Individuals carrying the rs3856806 T allele had a 1.366 fold increased risk of ischemic stroke(OR = 1.366, 95% CI: 1.077–1.733, P = 0.010). The distribution frequencies of the PPARG gene haplotypes rs1801282-rs3856806 in the control and case groups were determined. The frequency of distribution in the G-T haplotype case group was significantly higher than that in the control group. The risk of ischemic stroke increased to 2.953 times in individuals carrying the G-T haplotype(OR = 2.953, 95% CI: 2.082–4.190, P < 0.001). The rs1801282 G allele and rs3856806 T allele had a multiplicative interaction(OR = 3.404, 95% CI: 1.631–7.102, P < 0.001) and additive interaction(RERI = 41.705, 95% CI: 14.586–68.824, AP = 0.860;95% CI: 0.779–0.940;S = 8.170, 95% CI: 3.772–17.697) on ischemic stroke risk, showing a synergistic effect. Of all ischemic stroke cases, 86% were attributed to the interaction of the G allele of rs1801282 and the T allele of rs3856806. The effect of the PPARG rs1801282 G allele on ischemic stroke risk was enhanced in the presence of the rs3856806 T allele(OR = 8.001 vs. 1.844). The effect of the rs3856806 T allele on ischemic stroke risk was also enhanced in the presence of the rs1801282 G allele(OR = 2.546 vs. 1.366). Our results confirmed that the G allele of the PPARG gene rs1801282 locus and the T allele of the rs3856806 locus may be independent risk factors for ischemic stroke in the Han population of northern China, with a synergistic effect between the two alleles.
基金Project (No. 2002AA517020) supported by the Hi-Tech Research and Development Program (863) of China
文摘Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.
文摘Introduces the ceramic stove controlling system controlled by MSP430 single-chip computer. The system ameliorate the PID control method, adopts CHAOS-RBF, improves the accuracy of temperature control largely. There are two parts in this system, the lower machine measures the data and the upper machine with responsibility for data processing, displaying data and so on. Mean-while, using the serial communication RS-485 to realize the control of principal and subordinate station.
