基于微粒群算法的非线性系统模型参数估计

微粒群优化(PSO)算法是一种进化算法,包含的概念简单.本文不同于传统的非线性模型参数估计方法,将微粒群优化算法应用于非线性系统模型(NSM)的参数估计,并通过重油热解三集总模型参数估计进行PSO算法效果测试.实验结果表明:微粒群算法为非线性系统模型参数估计提供了一种新方法.     

基于粒子群优化算法的非线性系统模型参数估计

非线性模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对3种典型的非线性模型的参数估计进行了验证。实验结果表明：粒子群优化算法参数估计精度高,是一种有效的参数估计方法。     

基于粒子群优化算法的非线性系统模型参数估计

非线性模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值.由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对3种典型的非线性模型的参数估计进行了验证.实验结果表明:粒子群优化算法参数估计精度高,是一种有效的参数估计方法.     

PSO算法在非线性回归模型参数估计中的应用

非线性回归模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对6种非线性回归模型的参数估计进行了验证。实验结果表明：粒子群优化算法是一种有效的参数估计方法。     

PSO算法在非线性回归模型参数估计中的应用

非线性回归模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值.由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对6种非线性回归模型的参数估计进行了验证.实验结果表明:粒子群优化算法是一种有效的参数估计方法.     

PSO算法在非线性模型参数估计中的应用

将微粒群优化(PSO)算法用于非线性系统模型参数估计，并通过对谷氨酸菌体生长模型的参数估计进行了验证．实验结果表明：微粒群算法为非线性系统模型参数估计提供了一种有效的途径．     

求解非线性方程组的社会认知算法

将非线性方程组的求解问题转化为函数优化问题,应用一种新的智能优化算法——社会认知算法求解此优化问题,实验结果表明了社会认知算法在求解非线性方程组时的可行性和有效性。     

非线性约束优化的算法分析

针对非线性约束优化问题,运用了一种新的智能优化算法——社会认知优化算法。社会认知优化算法是一种基于社会认知理论的集群智能优化算法,它对目标函数的解析性质没有要求,适合于大规模约束问题处理的优点,使搜索不容易陷入局部最优。将该算法引入非线性约束问题,解决优化问题。通过实例和其他算法进行比较,对比数值实验结果表明,即使只有一个学习主体,该算法能够高效、稳定地得到解决方案,便于求解非线性约束优化问题。     

求解非线性互补问题的熵函数认知优化算法

提出了一个求解非线性互补问题的熵函数社会认知优化算法。首先将非线性互补问题转化为非线性方程组来求解,然后利用熵函数法将非线性方程组求解转化为一个光滑的无约束优化问题,最后应用社会认知优化算法求解此优化问题。实验结果表明,该算法收敛速度快,稳定性好,是求解非线性互补问题的一种有效算法。     

神经网络在线投影算法及非线性建模应用

针对神经网络难以在线学习的缺点,把神经网络当作结构已知的非线性系统,权系数的学习看成非线性系统的参数估计,基于新估计准则的非线性系统在线参数估计投影算法,给出前馈神经网络的一种在线运行投影学习算法.理论上证明该算法的全局收敛性,讨论算法参数的物理意义和取值范围.通过2个非线性时变系统的神经网络建模应用的仿真,验证算法的全局收敛性和在线运行能力.     

用自组织迁移算法改进的社会认知优化算法

结合自组织迁移算法和社会认知优化的优点,在社会认知优化中融入自组织迁移的过程,通过增加2个参数协调两者的优化进程。经过大量的实验确定这2个参数的适当取值,完善了算法。该算法对最终的优化结果产生的影响微小,且在早期可以就获得较快的收敛　　　速度。     

FHOSM Tolerant Control for Networked Control Systems with Disturbances and Faults

This paper investigates the networked control system (NCS) with faults and disturbances which affect both actuator and sensor. A fast high-order sliding mode (FHOSM) controller is proposed to compensate for actuator faults and sensor disturbances, which is constructed based on the estimated information of the NCS. Accordingly, an adaptive observer with multi-stage is designed to estimate the states and sensor disturbances and protect the NCS from actuator faults. Furthermore, a new sliding function is assembled to realize the finite-time convergence of system states. The stability of the system with the suggested procedure is illustrated by the stability analysis underneath the designed control law. Finally, the simulation results are implemented and confirm the effectiveness of the proposed method in defending the system against both issues and inhibiting system failures.

     

网络化控制系统的研究现状与展望

随着网络技术的发展，网络化控制系统日益成为人们关注的课题，特别是目前工业以太网所取得的进展引起人们广泛兴趣。介绍了网络化控制系统的发展过程，在综述网络化控制系统研究现状的基础上，对网络化控制系统的建模、状态估计、信息时延、调度算法、通信约束等方面进行了深入分析，并对其中的建模、网络信息调度的最优化、网络环境下控制理论研究、应用研究等方面的未来发展作了展望。     

Networked control with delay measurement and estimation

In research on networked control systems (NCSs), the performance of the control system is assumed to be affected by network-induced parameters such as delays, jitters, packet losses and link failures. In this paper, compensation methods are studied, in the framework of NCS while considering the protocol specifications. The aim is to propose two different methods for the control over a network, adaptive Smith predictor approach and robust control based approach, which are imposed by the accessible information about the network. First, a clock synchronization protocol is implemented in each device of the network, thereby providing the possibility of end-to-end delay measurement, and a compensation strategy based on this measurement and the adaptive Smith predictor approach is applied. Secondly, the end-to-end upper bound delay estimate is calculated on the basis of the network architecture and the traffic estimation, and then subsequently used in a robust control based compensation strategy. The switched Ethernet network is used as an example of the communication network of the NCS. The efficiency of proposed approaches is evaluated with Matlab simulation as well as in the experimental prototype.     

网络控制系统中的时戳预测函数控制

传统预测函数控制算法不能有效处理网络控制系统中的随机延迟,为此提出了时戳预测函数控制算法.该算法使用时间戳来估计由网络引入的控制延迟,在预测系统未来输出时明确考虑了该延迟,然后借鉴传统预测函数控制算法的基本策略,得到了适用于网络控制系统的控制规律.进一步由TrueT im e工具箱搭建了网络控制系统仿真平台,对比了时戳预测函数控制算法和传统预测函数控制算法.仿真结果表明随着网络引入延迟的增大,时戳预测函数控制算法给出控制品质明显优越于传统预测函数控制算法.     

Robust stability analysis of networked systems with varying delays

This article is concerned with networked controlled systems (NCS) with uncertain, varying, bounded transmission delays and asynchronous discrete-time static control laws. It is first shown that the delay variation gives rise to a discrete-time uncertain NCS model; robust stability analysis is carried out via a linear matrix inequality approach which, when combined with a directed parameter search, yields an estimate of robust stability bounds against any variations of the maximum allowable delay (constrained within one sampling period) that the closed-loop system can tolerate. The derived bounds are compared with other techniques relying on the singular values of the perturbed NCS-model. The presented simulation results prove the efficacy of the proposed control scheme.     

数据包丢失网络控制系统的保成本控制

针对同时具有不确定短时延及数据包丢失的网络控制系统,引入丢包补偿器补偿丢包对系统的影响,当发生数据包丢失时,控制器采用丢包补偿器产生的预估状态代替系统的真实状态计算控制量。同时考虑不确定短时延的影响,将网络控制系统建模为一类具有参数不确定的离散切换系统．利用线性矩阵不等式(linear matrix inequality,简称LMI)方法给出了系统的保成本控制器和丢包补偿器的协同设计方法,仿真结果表明所提出的方法是有效的。     

Kalman filter based fault diagnosis of networked cont rol system with white noise

The networked control system NCS is regarded as a sampled control system with output time- variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters ,a Kalman filter is constructed for this NCS. By comparing the output of the filter and the practical system ,a residual is generated to diagnose the sensor faults and the actuator faults. Finally ,an example is given to show the feasibility of the approach.     

Kalman filter based fault diagnosis of networked control system with white noise

The networked control system NCS is regarded as a sampled control system with output time-variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters, a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults. Finally, an example is given to show the feasibility of the approach.