A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An...A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.展开更多
为克服PI算法所存在的响应速度慢,对网络参数变化敏感的缺点,将神经网络理论引入主动队列管理的研究中,提出一种基于单神经元的主动队列管理算法NPI(Neuron based PI)。NPI算法将PI控制器看成是二输入的ADALINE神经元,控制器的比例系数...为克服PI算法所存在的响应速度慢,对网络参数变化敏感的缺点,将神经网络理论引入主动队列管理的研究中,提出一种基于单神经元的主动队列管理算法NPI(Neuron based PI)。NPI算法将PI控制器看成是二输入的ADALINE神经元,控制器的比例系数和积分系数按照LMS算法进行在线调整,对网络状态的变化有自学习能力,使队列长度能够快速收敛到目标值,并增强了队列的稳定性。仿真试验结果表明NPI算法比PI有更好的性能。展开更多
基于神经网络理论中的神经元模型与学习算法,设计了一种主动队列管理算法SNAPI(Single Neuron-based Adaptive PI controller).控制器根据系统误差在线调整PI控制器的控制参数,以适应动态变化的网络参数.运用Nyquist稳定判据给出了系统...基于神经网络理论中的神经元模型与学习算法,设计了一种主动队列管理算法SNAPI(Single Neuron-based Adaptive PI controller).控制器根据系统误差在线调整PI控制器的控制参数,以适应动态变化的网络参数.运用Nyquist稳定判据给出了系统在平衡点附近的局部稳定条件.最后通过仿真检验了SNAPI,并比较了它与使用固定控制参数的PI算法的性能.展开更多
Control design is important for PEMFC （proton exchange membrane fuel cell） distributed power generator to satisfy user requirement for safe and stable operation. For a complex multi-variable dynamic system, a dynami...Control design is important for PEMFC （proton exchange membrane fuel cell） distributed power generator to satisfy user requirement for safe and stable operation. For a complex multi-variable dynamic system, a dynamic simulation model is first established. In view of close coupling and non-linear relationships between variables, the intelligent auto-adapted PI decoupling control method is used. From the simulation results it is found that, by bringing quadratic performance index in the single neuron, constructing adaptive PI controller, and adjusting gas flow rates through the second pressure relief valve and air compressor coordinately, both anode and cathode pressures can be maintained at ideal levels.展开更多
基金Project(2010GK3091) supported by Industrial Support Project in Science and Technology of Hunan Province, ChinaProject(10B058) supported by Excellent Youth Foundation Subsidized Project of Hunan Provincial Education Department, China
文摘A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.
文摘为克服PI算法所存在的响应速度慢,对网络参数变化敏感的缺点,将神经网络理论引入主动队列管理的研究中,提出一种基于单神经元的主动队列管理算法NPI(Neuron based PI)。NPI算法将PI控制器看成是二输入的ADALINE神经元,控制器的比例系数和积分系数按照LMS算法进行在线调整,对网络状态的变化有自学习能力,使队列长度能够快速收敛到目标值,并增强了队列的稳定性。仿真试验结果表明NPI算法比PI有更好的性能。
文摘基于神经网络理论中的神经元模型与学习算法,设计了一种主动队列管理算法SNAPI(Single Neuron-based Adaptive PI controller).控制器根据系统误差在线调整PI控制器的控制参数,以适应动态变化的网络参数.运用Nyquist稳定判据给出了系统在平衡点附近的局部稳定条件.最后通过仿真检验了SNAPI,并比较了它与使用固定控制参数的PI算法的性能.
基金Project supported by National High-Technology Research andDevelopment Program of China (Grant No .2002AA517020)
文摘Control design is important for PEMFC （proton exchange membrane fuel cell） distributed power generator to satisfy user requirement for safe and stable operation. For a complex multi-variable dynamic system, a dynamic simulation model is first established. In view of close coupling and non-linear relationships between variables, the intelligent auto-adapted PI decoupling control method is used. From the simulation results it is found that, by bringing quadratic performance index in the single neuron, constructing adaptive PI controller, and adjusting gas flow rates through the second pressure relief valve and air compressor coordinately, both anode and cathode pressures can be maintained at ideal levels.
