基于改进量子粒子群算法的NCS模糊控制器参数优化

针对网络化控制系统中模糊控制器的量化因子和比例因子采用传统经验方法难以整定的问题, 提出了一种改进量子粒子群（IQPSO）算法对模糊控制器量化因子和比例因子进行优化。该方法将ABC算法中的搜索算子作为变异算子引入到QPSO算法中, 使得IQPSO算法较好地克服了QPSO算法保持种群多样性差容易早熟收敛的缺陷, 并以ITAE指标作为IQPSO算法的适应度函数对模糊控制器进行优化。典型工业过程仿真结果表明, IQPSO优化的模糊控制器具有比PID控制器和标准QPSO优化的模糊控制器更好的控制性能和适用性。     

A fuzzy logic based supervisory hierarchical control scheme for real time pressure control

This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.     

网络时延的模糊逻辑补偿方法的研究

当网络应用到控制系统中时，网络将引起时延，从而对闭环网络控制系统产生一些不利的影响，比如系统性能下降，系统不稳定等。本文介绍了通过在已有的PI控制器的基础上，再增加一个模糊逻辑补偿器来补偿网络控制系统中网络所引起的时延，其优点是不需要再重新设计已有的PI控制器，而只是简单地将模糊逻辑控制器的输出作为一个参数来调节PI控制器所提供的控制信号。文中采用了MATLAB／SIMULINK仿真，仿真结果表明了该方法的有效性。     

网络时延的模糊逻辑补偿方法的研究

当网络应用到控制系统中时,网络将引起时延,从而对闭环网络控制系统产生一些不利的影响,比如系统性能下降,系统不稳定等。本文介绍了通过在已有的PI控制器的基础上,再增加一个模糊逻辑补偿器来补偿网络控制系统中网络所引起的时延,其优点是不需要再重新设计已有的PI控制器,而只是简单地将模糊逻辑控制器的输出作为一个参数来调节PI控制器所提供的控制信号。文中采用了MATLAB/SIMULINK仿真,仿真结果表明了该方法的有效性。     

Imperfect premise matching controller design for T-S fuzzy systems under network environments

This paper focuses on an imperfect premise matching controller design for T-S fuzzy systems under network environments. Different with the traditional parallel distribution compensation (PDC) method, the same premises between the PDC controller and the T-S fuzzy systems are no longer needed again in the proposed method. Under consideration of the unmatched grades of membership in the networked T-S fuzzy systems, a unified T-S fuzzy model is firstly proposed, in which a networked state-feedback fuzzy controller with communication delays is used to reconstruct the system. Then, based on the constructed model and by use of the Wirtinger-based inequality technique to deal with the cross items, two less conservative stability and stabilization criteria are derived to enhance the design flexibility. Finally, two numerical examples are used to show the effectiveness of proposed method.     

基于自适应模糊与输入输出线性化的卫星姿态控制

将自适应模糊控制与输入输出线性化控制相结合，构成混合控制器，并将其应用于挠性卫星的姿态机动控制．给出了卫星姿态控制器的基本形式，分析了控制器参数的选取准则．在线调节自适应模糊控制器的参数，以补偿不确定性卫星的姿态跟踪误差．仿真结果表明，该控制算法通过在线学习能有效地克服挠性卫星的不确定性，具有较强的鲁棒性，从而有效地提高了挠性卫星的姿态控制精度．     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

Linearizing control of induction motor based on networked control systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.     

网络控制系统的模糊自适应PID控制

针对网络控制系统中网络时延补偿的问题,提出了一种模糊自适应PID控制器的设计方法,通过利用在线时延估计方法对时延进行预估计,根据估计时延值在线调节PID三个参数,从而改善系统的性能。对基于控制局域网络(CAN)总线的典型工业过程进行了仿真实验。结果表明,与常规PID的控制器比较,采用设计的PID控制器能补偿网络上的时延,更加有效的抑制了时延对网络控制系统的影响并提高了系统的性能。     

基于T-S模型的网络控制系统故障诊断

针对一类参数不确定并具有时延和丢包情况的非线性网络控制系统,为达到快速准确的进行故障诊断的目的,提出了一种基于T-S模糊模型的故障诊断方法.通过建立此系统的T-S模糊模型,利用平行分布补偿时延的思想设计了满足系统稳定性条件的状态反馈控制器,以及通过引入随机切换系统表示数据有无丢失情况下的基于模糊观测器的鲁棒故障诊断方法,然后基于 Lyapunov函数和线性矩阵不等式方法给出了该闭环网络控制系统渐近稳定的充分条件,最后通过仿真例子验证了该方法能够使闭环控制系统渐进稳定以及能够准确的进行故障诊断,验证了所设计方法的有效性.     

A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general Multi-Input Multi-Output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters.     

基于T-S模糊模型非线性网络控制系统改进H∞跟踪控制

研究一类非线性网络控制系统改进H∞跟踪控制问题, 该类网络控制系统中非线性被控对象和被跟踪对象分别采用Takagi-Sugeno(T-S)模糊模型和线性稳定参考模型描述. 首先通过综合考虑网络中的数据传输时滞和数据丢包影响, 采用输入时滞法和并行分布补偿技术, 建立基于零阶保持器刷新时刻的系统状态跟踪误差模型. 然后利用改进的自由权矩阵方法, 并结合Lyapunov直接法给出系统满足H∞跟踪性能的充分条件以及模糊控制器的设计方法. 最后仿真实例表明本文方法的有效性和相比已有方法的优越性.     

机械臂自调整因子模糊PD迭代学习控制研究

大部分模糊控制器不具有适应控制对象变化的能力,基于此设计一种自调整因子模糊控制器,并针对机械臂长时间重复操作导致运动精确度下降这一类问题,结合迭代学习控制方法,提出一种自调整因子模糊PD迭代学习控制方法;以双关节机械臂为研究对象,利用Fuzzy工具箱编写模糊控制规则,通过系统产生的误差以及误差的变化率作为模糊控制器的输入量调整模糊系统中的量化因子和比例因子,实现模糊规则的更新和对迭代学习控制中的PD参数的实时调整,系统的自适应性得到提高,并在Simulink中进行机械臂的运动控制实验,仿真结果表明,所提控制方法最终产生的误差可以精确到0.0001 rad,同时在进行第2次迭代时关节角度和角速度误差收敛基本趋于零,整体的控制效果较好。     

利用模糊系统的自适应模糊控制器

针对非线性系统控制,设计了利用TSK(Takagi-Sugeno-Kang)模糊系统的自适应模糊控制器。所设计的自适应控制方法是参考模型自适应控制方法,而且利用Lyapunov函数保证了闭环系统的稳定性,同时推导了最优的自适应控制规律。首先,根据控制对象的输入输出数据建立TSK模糊模型,然后,由TSK模糊模型设计初期的TSK模糊控制器,并根据自适应规律随时调整模糊控制器参数。倒立摆系统的仿真实验验证了所设计的自适应模糊控制器的有效性。     

A new error handling algorithm for controller area network in networked control system

An effective error handling mechanism plays an important role to ensure the reliability and robustness of the application of controller area network (CAN) in controlling dynamic systems. This paper addresses a new online error handling approach or named per-sample-error-counting (PSeC) technique that tends to replace native error handling protocol in controller area network (CAN). The mechanism is designed to manage transmission errors of both sensor and control data in networked control system (NCS) used in controlling dynamic system such that the stability of the feedback system is preserved. A new parameter denoted as maximum allowable number of error burst (MAEB) is introduced in which MAEB is selected based on available bandwidth of the CAN network. MAEB serves as the maximum number of attempt of re-transmission of erroneous data per sample which allows the maximum transmission period to be known and guaranteed for time-critical control system. The efficacy of the proposed method is verified by applying the algorithm on the fourth order inverted pendulum system simulated on Matlab/Truetime simulator and the performance is benchmarked with the existing CAN error management protocol. The simulation run under various systems conditions demonstrate that the proposed method results in superior system performance in handling data transmission error as well as meeting control system requirement.     

一类未知非线性离散系统的直接自适应模糊预测控制

将自适应模糊逻辑系统引入预测控制,对一类未知非线性离散系统提出了直接自适应 模糊预测控制方法.首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直 接利用模糊逻辑系统设计预测控制器,并基于广义误差估计值对控制器参数和广义误差估计值中 的未知向量进行自适应调整.文中证明了此方法可使广义误差估计值收敛到原点的小邻域内.     

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.     

时滞网络控制系统的模糊控制及稳定性分析

针对网络控制系统(Networked Control Systems,NCSs)中存在的时延问题,给出一种新的网络控制系统的建模方法--离散模糊T-S模型,利用此模型去逼近非线性网络控制系统,并在此模型的基础上应用平行分布补偿原理(Parallel Distributed Compensation,PDC)设计控制器....     

网络控制系统的时延相关状态反馈控制器设计

研究了具有时延、丢包和数据包时序错乱的网络控制系统镇定问题.为了有效提高系统的控制性能,本文根据网络控制系统的特点提出一种时延相关状态反馈控制方法,并将闭环网络控制系统建模为离散时间切换模型.在此基础上,通过构造依赖于参数的Lyapunov函数给出了闭环网络控制系统的稳定条件和镇定控制器设计方法.仿真结果和实验结果表明所提方法的有效性和可用性.     

基于量子遗传算法的模糊滑模控制

针对一类具有不确定性的非线性系统,提出了一种新的基于量子遗传算法的模糊滑模控制器的设计方法.将模糊控制与滑模控制相结合,利用滑模控制使系统跟踪误差进入边界层内;启用模糊控制替代切换控制,并在边界层上通过监督函数平滑控制作用.在滑动模态产生条件下,通过量子遗传算法优化模糊控制器的控制规则,有效地解决了模糊滑模控制中模糊控制规则的确定问题,从而削弱了系统的抖振,改善了控制器的控制性能.仿真结果表明了该方法的有效性.