基于离散事件触发通讯机制(DETCS,discrete event-triggered communication scheme),研究了执行器故障下不确定非线性网络化控制系统的鲁棒H∞保性能容错控制与通讯协同设计问题。引入DETCS,采用状态反馈控制策略,建立了闭环故障系统模...基于离散事件触发通讯机制(DETCS,discrete event-triggered communication scheme),研究了执行器故障下不确定非线性网络化控制系统的鲁棒H∞保性能容错控制与通讯协同设计问题。引入DETCS,采用状态反馈控制策略,建立了闭环故障系统模型;结合Lyapunov稳定性理论及少保守性技术,得到了DETCS下使系统具有鲁棒H∞保性能容错控制的时滞/事件依赖的充分条件,提供控制器与事件触发权矩阵协同优化求解的方法,讨论了几种不同目标约束下鲁棒控制与DETCS的协同设计问题;通过仿真实例验证了所述方法的有效性。展开更多
基于离散事件触发通讯机制,研究了在稀疏数据传输情形下非线性网络化控制系统(Nonlinear Networked Control System,NNCS)的满意容错控制问题。基于事件触发稀疏数据传输背景建立了带执行器饱和约束的闭环故障系统模型;利用提出的安全...基于离散事件触发通讯机制,研究了在稀疏数据传输情形下非线性网络化控制系统(Nonlinear Networked Control System,NNCS)的满意容错控制问题。基于事件触发稀疏数据传输背景建立了带执行器饱和约束的闭环故障系统模型;利用提出的安全度定义和Lyapunov稳定性理论,推证出使NNCS具有广义H_∞/H_2性能和α-安全度的容错收缩不变集充分条件和相应的协同设计方法;通过仿真算例验证了理论方法在协同设计方面的可行性和节约网络资源方面的有效性,并提出了实际应用中应遵循的折中类定性原则。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 invertible of the Large Air Dense Medium Fluidized Bed (ADMFB) were studied by introducing the concept of the inverse system theory of nonlinear systems. Then the ADMFB, which was a multivariable, nonlinear and co...The invertible of the Large Air Dense Medium Fluidized Bed (ADMFB) were studied by introducing the concept of the inverse system theory of nonlinear systems. Then the ADMFB, which was a multivariable, nonlinear and coupled strongly system, was decoupled into independent SISO pseudo-linear subsystems. Linear controllers were designed for each of subsystems based on linear systems theory. The practice output proves that this method improves the stability of the ADMFB obviously.展开更多
文摘基于离散事件触发通讯机制(DETCS,discrete event-triggered communication scheme),研究了执行器故障下不确定非线性网络化控制系统的鲁棒H∞保性能容错控制与通讯协同设计问题。引入DETCS,采用状态反馈控制策略,建立了闭环故障系统模型;结合Lyapunov稳定性理论及少保守性技术,得到了DETCS下使系统具有鲁棒H∞保性能容错控制的时滞/事件依赖的充分条件,提供控制器与事件触发权矩阵协同优化求解的方法,讨论了几种不同目标约束下鲁棒控制与DETCS的协同设计问题;通过仿真实例验证了所述方法的有效性。
文摘基于离散事件触发通讯机制,研究了在稀疏数据传输情形下非线性网络化控制系统(Nonlinear Networked Control System,NNCS)的满意容错控制问题。基于事件触发稀疏数据传输背景建立了带执行器饱和约束的闭环故障系统模型;利用提出的安全度定义和Lyapunov稳定性理论,推证出使NNCS具有广义H_∞/H_2性能和α-安全度的容错收缩不变集充分条件和相应的协同设计方法;通过仿真算例验证了理论方法在协同设计方面的可行性和节约网络资源方面的有效性,并提出了实际应用中应遵循的折中类定性原则。
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.
文摘The invertible of the Large Air Dense Medium Fluidized Bed (ADMFB) were studied by introducing the concept of the inverse system theory of nonlinear systems. Then the ADMFB, which was a multivariable, nonlinear and coupled strongly system, was decoupled into independent SISO pseudo-linear subsystems. Linear controllers were designed for each of subsystems based on linear systems theory. The practice output proves that this method improves the stability of the ADMFB obviously.
