A Fuzzy Neural Network PID Controller is proposed in this paper,Fuzzy Neural Network Controller is used to optimize the parameters of PID Controller real time.Computer simulation using MATLAB shows that,comparing to t...A Fuzzy Neural Network PID Controller is proposed in this paper,Fuzzy Neural Network Controller is used to optimize the parameters of PID Controller real time.Computer simulation using MATLAB shows that,comparing to the classical PID Controller,Fuzzy Neural Network PID Controller can improve the precision of control results for seam tracking.展开更多
In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neura...In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.展开更多
A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simul...A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simula-tion results show that the controller has not only simple nonlinear control of tfuzzy control, but also the learning capabil-ity and adaptability of neural netwrk.展开更多
In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance...In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.展开更多
为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。...为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。展开更多
为提高氮化炉温度控制精度,提出一种基于模糊神经网络PID的氮化炉温度控制方法。即采用模糊神经网络对PID控制的K_(p)、K_(i)、K_( d )3个控制参数进行优化整定,而考虑到模糊神经网络的局限,采用改进后的CS算法对模糊神经网络的初始权...为提高氮化炉温度控制精度,提出一种基于模糊神经网络PID的氮化炉温度控制方法。即采用模糊神经网络对PID控制的K_(p)、K_(i)、K_( d )3个控制参数进行优化整定,而考虑到模糊神经网络的局限,采用改进后的CS算法对模糊神经网络的初始权值和阈值进行优化;最后,根据设计的基于模糊神经网络的PID控制方法搭建新的氮化炉温度控制器,并进行试验测试。试验结果表明:采用FA算法的吸引力机制对CS算法进行改进的方法可以有效提高CS算法的搜索能力和收敛速度;而改进后的CS算法在经过6次迭代后适应度值趋近于零,快速达到平稳,获取最优模糊神经网络的初始权值和阈值;优化后PNN-PID控制曲线在110 s后便达到稳定,与传统PID控制曲线相比,调整时间大幅下降;优化后PNN-PID控制器输出的氮化炉温度控制阶跃信号,其调整时间为960 s左右,与传统PID控制曲线相比,响应速度明显提升,且与系统给出预期温度控制信号曲线几乎完全重叠,控制误差极小,实现了提高氮化炉温度控制精度的目的。展开更多
基金National natural sciences fund(50075037)JiangXi province natural sciencesfund(550079)
文摘A Fuzzy Neural Network PID Controller is proposed in this paper,Fuzzy Neural Network Controller is used to optimize the parameters of PID Controller real time.Computer simulation using MATLAB shows that,comparing to the classical PID Controller,Fuzzy Neural Network PID Controller can improve the precision of control results for seam tracking.
基金Sponsored by National High-tech Research and Development Project of China(2009AA04Z143)Natural Science Foundation of Hebei Province of China(E2006001038)Science and Technology Project of Hebei Province of China(10212101D)
文摘In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.
文摘A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simula-tion results show that the controller has not only simple nonlinear control of tfuzzy control, but also the learning capabil-ity and adaptability of neural netwrk.
文摘In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.
文摘为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。
文摘为提高氮化炉温度控制精度,提出一种基于模糊神经网络PID的氮化炉温度控制方法。即采用模糊神经网络对PID控制的K_(p)、K_(i)、K_( d )3个控制参数进行优化整定,而考虑到模糊神经网络的局限,采用改进后的CS算法对模糊神经网络的初始权值和阈值进行优化;最后,根据设计的基于模糊神经网络的PID控制方法搭建新的氮化炉温度控制器,并进行试验测试。试验结果表明:采用FA算法的吸引力机制对CS算法进行改进的方法可以有效提高CS算法的搜索能力和收敛速度;而改进后的CS算法在经过6次迭代后适应度值趋近于零,快速达到平稳,获取最优模糊神经网络的初始权值和阈值;优化后PNN-PID控制曲线在110 s后便达到稳定,与传统PID控制曲线相比,调整时间大幅下降;优化后PNN-PID控制器输出的氮化炉温度控制阶跃信号,其调整时间为960 s左右,与传统PID控制曲线相比,响应速度明显提升,且与系统给出预期温度控制信号曲线几乎完全重叠,控制误差极小,实现了提高氮化炉温度控制精度的目的。
