HCN单激光器和转动光栅形成的差频信号一般不大于100 k Hz.HCN双激光器较之单激光器和转动光栅系统可以很容易的获得MHz量级的差频信号,进而可以研究更高时间分辨率的物理现象.但是HCN双激光器功率和频率都不稳定,功率稳定是频率稳定的...HCN单激光器和转动光栅形成的差频信号一般不大于100 k Hz.HCN双激光器较之单激光器和转动光栅系统可以很容易的获得MHz量级的差频信号,进而可以研究更高时间分辨率的物理现象.但是HCN双激光器功率和频率都不稳定,功率稳定是频率稳定的前提,所以必须先保证HCN双激光器功率的稳定.本文以稳定功率为目标结合双激光器的特点,设计了一套双激光器功率的自动反馈控制系统及远程控制系统.本系统主要有西门子S7-200PLC、电动平台、肖特基势垒二极管和上位机等组成.实验表明这种方法可以长时间地使HCN双激光器稳定在高功率状态.展开更多
Abstract--In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assum...Abstract--In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks (NNs), non- linear pure-feedback systems, time-varying constraints.展开更多
The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning co...The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.展开更多
This paper is concerned with the issue of stabilization for the linear neutral systems with mixed delays. The attention is focused on the design of output feedback controllers which guarantee the asymptotical stabilit...This paper is concerned with the issue of stabilization for the linear neutral systems with mixed delays. The attention is focused on the design of output feedback controllers which guarantee the asymptotical stability of the closed-loop systems. Based on the model transformation of neutral type, the Lyapunov-Krasovskii functional method is employed to establish the delay-dependent stability criterion. Then, through the controller parameterization and some matrix transformation techniques, the desired parameters are determined under the delay-dependent design condition in terms of linear matrix inequalities (LMIs), and the desired controller is explicitly formulated. A numerical example is given to illustrate the effectiveness of the proposed method.展开更多
透析相关性低血压(IDH)会降低透析的疗效,给患者带来不适,其发生率为20%~30%[1]。IDH的诊断必须满足下述两个条件之一:透析过程中收缩压至少下降20 mm Hg(1mm Hg=0.133k Pa),或平均动脉压至少下降10 mm Hg。IDH与多种临床事件相关...透析相关性低血压(IDH)会降低透析的疗效,给患者带来不适,其发生率为20%~30%[1]。IDH的诊断必须满足下述两个条件之一:透析过程中收缩压至少下降20 mm Hg(1mm Hg=0.133k Pa),或平均动脉压至少下降10 mm Hg。IDH与多种临床事件相关,如心律失常、血管通路栓塞和脑血管循环、肠系膜循环和冠脉循环缺血[2]。展开更多
This paper investigates adaptive state feedback stabilization for a class of feedforward nonlinear systems with zero-dynamics, unknown linear growth rate and control coefficient. For design convenience, the state tran...This paper investigates adaptive state feedback stabilization for a class of feedforward nonlinear systems with zero-dynamics, unknown linear growth rate and control coefficient. For design convenience, the state transformation is first introduced and the new system is obtained. Then, the estimation law is constructed for the unknown control coefficient, and the state feedback controller is proposed with a gain updated on-line. By appropriate choice of the estimation law for the control coefficient and the dynamic gain, the states of the closed-loop system are globally bounded, and the state of the original system converges to zero. Finally, a simulation example is given to illustrate the correctness of the theoretical results.展开更多
文摘HCN单激光器和转动光栅形成的差频信号一般不大于100 k Hz.HCN双激光器较之单激光器和转动光栅系统可以很容易的获得MHz量级的差频信号,进而可以研究更高时间分辨率的物理现象.但是HCN双激光器功率和频率都不稳定,功率稳定是频率稳定的前提,所以必须先保证HCN双激光器功率的稳定.本文以稳定功率为目标结合双激光器的特点,设计了一套双激光器功率的自动反馈控制系统及远程控制系统.本系统主要有西门子S7-200PLC、电动平台、肖特基势垒二极管和上位机等组成.实验表明这种方法可以长时间地使HCN双激光器稳定在高功率状态.
基金supported in part by the National Natural Science Foundation of China(61622303,61603164,61773188)the Program for Liaoning Innovative Research Team in University(LT2016006)+1 种基金the Fundamental Research Funds for the Universities of Liaoning Province(JZL201715402)the Program for Distinguished Professor of Liaoning Province
文摘Abstract--In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks (NNs), non- linear pure-feedback systems, time-varying constraints.
基金Project(51074051)supported by the National Natural Science Foundation of China
文摘The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.
基金the National Natural Science Foundation of China (No. 50708094)the Hi-Tech Research and Development Program (863) of China (No. 2007AA11Z216)
文摘This paper is concerned with the issue of stabilization for the linear neutral systems with mixed delays. The attention is focused on the design of output feedback controllers which guarantee the asymptotical stability of the closed-loop systems. Based on the model transformation of neutral type, the Lyapunov-Krasovskii functional method is employed to establish the delay-dependent stability criterion. Then, through the controller parameterization and some matrix transformation techniques, the desired parameters are determined under the delay-dependent design condition in terms of linear matrix inequalities (LMIs), and the desired controller is explicitly formulated. A numerical example is given to illustrate the effectiveness of the proposed method.
文摘透析相关性低血压(IDH)会降低透析的疗效,给患者带来不适,其发生率为20%~30%[1]。IDH的诊断必须满足下述两个条件之一:透析过程中收缩压至少下降20 mm Hg(1mm Hg=0.133k Pa),或平均动脉压至少下降10 mm Hg。IDH与多种临床事件相关,如心律失常、血管通路栓塞和脑血管循环、肠系膜循环和冠脉循环缺血[2]。
基金supported by the National Natural Science Foundations of China under Grant Nos.61104069,61325016,61273084,61374187 and 61473176Independent Innovation Foundation of Shandong University under Grant No.2012JC014
文摘This paper investigates adaptive state feedback stabilization for a class of feedforward nonlinear systems with zero-dynamics, unknown linear growth rate and control coefficient. For design convenience, the state transformation is first introduced and the new system is obtained. Then, the estimation law is constructed for the unknown control coefficient, and the state feedback controller is proposed with a gain updated on-line. By appropriate choice of the estimation law for the control coefficient and the dynamic gain, the states of the closed-loop system are globally bounded, and the state of the original system converges to zero. Finally, a simulation example is given to illustrate the correctness of the theoretical results.