发电机组汽门系统的多模型自学习控制

针对发电机组的非线性、大范围运行等实际问题，研究了用于汽门系统的多模型自学习控制（MMSC），首先根据各种工况下的样本数据归纳出模糊控制规则；然后由模糊聚类算法将多种工况约简为典型工况，得到相应的子模型模糊控制器（FLC）．以子模型FLC输出的加权集成作为MMSC的控制输出，而加权系数取决干子模型匹配度．在子模型FLC学习优化中，由支持向量机离线逼近模糊规则曲面，再由梯度下降算法在线自学习．仿真实验验证了所设计控制器的优良性能．     

基于改进QPSO的两轮移动机器人区间二型模糊逻辑控制

以两轮移动机器人(TWMR)为对象,针对机器人的非线性模型分别设计控制机器人平衡和位置的区间二型模糊逻辑控制器(IT2 FLC).针对区间二型模糊规则中参数难以设定的问题,通过改进的量子粒子群算法(LTQPSO)优化区间二型模糊集参数,并给出优化算法的流程图.针对区间二型模糊逻辑控制器和一型模糊逻辑控制器(T1 FLC)对平衡和位置的控制效果进行对比.进一步考虑质量不确定和位置扰动对两种控制器控制效果的影响.仿真结果表明,IT2 FLC可以有效地达到设定的控制目标,与T1 FLC相比,IT2 FLC拥有更好的处理不确定性的能力以及更强的抗扰动能力.     

基于FPSO的电力巡检机器人的广义二型模糊逻辑控制

针对电力巡检机器人(Power-line inspection robot, PLIR)的平衡调节问题, 设计了广义二型模糊逻辑控制器(General type-2 fuzzy logic controller, GT2FLC); 针对GT2FLC中隶属函数参数难以确定的问题, 通过模糊粒子群(Fuzzy particle swarm optimization, FPSO)算法来优化隶属函数参数. 将GT2FLC的控制性能与区间二型模糊逻辑控制器(Interval type-2 fuzzy logic controller, IT2FLC)和一型模糊逻辑控制器(Type-1 fuzzy logic controller, T1FLC) 的控制性能进行对比. 除此之外, 还考虑了外部干扰对三种控制器控制效果的影响. 仿真结果表明, GT2FLC具有更好的性能和处理不确定性的能力.     

基于量子粒子群的斜坡上两轮移动机器人的广义二型模糊控制

两轮移动机器人(2WMR)本身具有多变量和非线性等特征,从而使其控制变得复杂。当2WMR在倾斜的表面上移动时,控制问题变得更加复杂。针对2WMR的非线性模型,设计2WMR的广义二型模糊逻辑平衡控制器和位置控制器。针对广义二型模糊逻辑控制器(GT2FLC)中前、后件中参数难以设定的问题,通过量子粒子群算法(QPSO)优化隶属函数中的参数。针对GT2FLC和区间二型模糊逻辑控制器(IT2FLC)在不同斜面上对移动2WMR的平衡和位置控制的效果进行进一步的对比分析,并干扰对控制效果的影响。仿真结果表明,GT2FLC具有更好的抗干扰能力。     

基于SelQPSO的WMR区间二型模糊逻辑控制

针对轮式移动机器人(WMR)的轨迹跟踪问题,首先根据WMR非线性模型设计了区间二型模糊逻辑控制器(Ⅱ2FLC);其次针对IT2FLC模糊规则中隶属函数参数难以确定问题,通过改进的量子粒子群算法(SelQPSO)优化IT2FLC的隶属函数参数.最后,将经过SelQPSO优化的IT2FLC控制效果分别与经过量子粒子群算法(QPSO)优化的IT2FLC、未经优化的IT2FLC以及T1FLC算法进行对比.此外,进一步考虑外部扰动分别对四种控制方法控制效果的影响.仿真结果表明,与另外三种控制方法相比,经过SelQPSO优化的IT2FLC具有更好的控制效果和抗干扰能力.     

结合融合函数的双轮机器人二型模糊控制

针对双轮自平衡机器人的运动控制,设计了区间二型模糊逻辑控制器(T2FLC),提出函数融合的方法,解决模糊控制器规则繁杂的问题.首先对双轮机器人进行运动学建模,针对机器人的数学模型,设计双闭环二型模糊自适应PID控制器,分别控制机器人的直立平衡和行走速度.将机器人的反馈变量进行函数融合,简化T2FLC的模糊规则.对设计的...     

多模型控制策略在化石燃料发电机组上的应用(英文)

火力发电厂中广泛使用的化石燃料发电机组(FFPU)具有非线性、强耦合、参数随工况变化等复杂特性,依据单个工作点处的线性模型设计的多变量控制策略常常导致控制效果不佳,甚至系统不能稳定运行。针对该问题,本文提出了一种有效的多模型控制策略,以改善FFPU大工况范围运行时的控制性能。首先,通过定义归一化特征值作为系统非线性测度,将FFPU工作运行区间划分成多个局部模型区域,在每个区域用线性模型表征系统特性;随后,根据每个局部区域的线性模型,实施一种基于FFPU特性而设计的包含静态前馈、PD前馈和模糊解耦的混合解耦补偿,进而采用模糊PID结构设计分散控制器;最后,依照电力负载跟随调度策略,模糊加权局部控制器得到全局控制器输出,使得系统在大工况范围运行时平滑切换。为实现该控制策略,采用了双层控制结构:上层为监督控制层,作用是根据负载要求进行设定点调度以及进行局部控制器的模糊切换;下层为直接控制层,实施混合解耦补偿和各回路的模糊PID控制。仿真表明,所提出的控制策略性能良好且实用有效。     

基于CPS框架的微粉生产过程多模型自适应控制

针对矿渣微粉（Ground granulated blast-furnace slag,GGBS）生产这一多变量、强耦合、多工况的复杂非线性过程,本文根据大量生产数据,提炼出矿渣微粉生产过程的三个典型工况.求解多工况多目标优化问题以求得最优设定值.建立多工况下的递归神经网数据驱动模型,并采用自适应动态规划方法,建立多个控制器,结合加权多模型控制,实现矿渣微粉生产过程在多工况切换情况下的自适应控制.通过过程运行优化、跟踪控制优化、通讯、工业以太网等信息资源与矿渣微粉生产物理资源之间的融合,构建基于信息物理系统（Cyber-physical system,CPS）的矿渣微粉生产优化控制系统.实验分析表明,本文提出的基于CPS的多模型自适应控制器,能够有效实现多工况条件下矿渣微粉生产过程的自适应控制,减小超调量,提高控制品质.     

基于多模型模糊自适应PID的无人机纵向姿态控制研究

针对无人机典型的非线性,且气动参数随着工作环境时变的问题,文中研究了基于模糊控制规则下的多模型模糊自适应控制方法,以实现对无人机纵向姿态控制;该方法对局部采用经典PID设计的子控制器进行加权处理,从而达到多控制器集的平滑切换,在利用S函数建立的无人机六自由度模型基础上进行控制仿真,得出仿真曲线;基于无人机纵向姿态俯仰角控制仿真结果表明,设计的多模型模糊自适应PID控制器具有较好的控制性能和鲁棒性。     

质子交换膜燃料电池动态建模及其双模控制

由于已提出的质子交换膜燃料电池(PEMFC)模型难于控制, 提出利用MATLAB/SIMULINK仿真工具进行PEMFC系统动态建模, 同时为实现对PEMFC系统输出电压的控制, 采用了基于模糊规则切换的模糊逻辑控制器(FLC)和比例积分微分控制器(PID)相结合的双模控制方式. 仿真结果证明该动态模型易于控制, 能够反映出PEMFC系统的动态输出特性, 而且验证了基于模糊规则切换的双模控制能够有效抑制扰动, 改善PEMFC系统的动态输出特性, 保证系统的稳定运行, 有助于对PEMFC系统的输出性能分析以及实时控制系统的设计.     

On the design of neural-fuzzy control system

This article presents a neural–network-based fuzzy logic control (NN–FLC) system. The NN–FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NN–FLC model. Computer simulations of the inverted pendulum controlled by NN–FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.13: 11–26, 1998     

多变量模糊系统控制设计及其在并行混合电动汽车中的应用

利用矩阵半张量积方法研究了多变量模糊系统模糊逻辑控制器的设计,并得到了若干新的结果.首先给出了模糊规则新的表示形式,基于该表示形式,构造了模糊逻辑控制器的结构矩阵,将复杂的模糊推理转变成了简单的代数等式.然后当模糊控制规则不完全时,建立了最小入度控制算法;当模糊控制规则不一致时,给出了相应的处理方法.最后将得到的结果应用到并行混合电动汽车(PHEV)能量管理和控制策略的模糊控制器设计.     

Approximated fuzzy logic controlled shunt active power filter for improved power quality

Shunt active power filters have been widely used for power quality improvement. With the advancement in artificial intelligence techniques, the applications of fuzzy logic‐based control systems have increased manifolds. This paper proposes a reduced rule fuzzy logic controller (FLC) in the voltage control loop of a shunt active power filter (APF), which is approximating a conventional large rule FLC. The difference between the controlled outputs of two controllers is compensated by proposed compensating factors. The dynamic response and harmonic compensation performance of proposed 4‐rule approximated fuzzy logic controller (AFLC) is compared with 25‐rule FLC. A three‐phase shunt APF is used for harmonic and reactive power compensation. The proposed scheme is tested with randomly varying single and multiple non‐linear loads. The simulation results presented under transient and steady‐state conditions confirm that the proposed 4‐rule AFLC efficiently approximates the 25‐rule FLC. The proposed control methodology takes less computational time and computational memory as the numbers of rules are reduced significantly.     

Parallelization of a fuzzy control algorithm using quantum computation

Quantum computation is proposed for the parallelization of a fuzzy logic control (FLC) algorithm. Quantum computation speeds up the fuzzy inference since serial operations between matrices of large dimensionality are now replaced by a one-step quantum addition or a quantum subtraction. The unitarity properties of the algorithm prove that the FLC stands for a simulator of a quantum computing machine.     

Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot

Ankle rehabilitation robots have recently attracted great attention since they provide various advantages in terms of rehabilitation process from the viewpoints of patients and therapists. This paper presents development and evaluation of a fuzzy logic based adaptive admittance control scheme for a developed 2-DOF redundantly actuated parallel ankle rehabilitation robot. The proposed adaptive admittance control scheme provides the robot to adapt resistance/assistance level according to patients' disability level. In addition, a fuzzy logic controller (FLC) is developed to improve the trajectory tracking ability of the rehabilitation robot subject to external disturbances which possibly occur due to human-robot interaction. The boundary scales of membership functions of the FLC are tuned using cuckoo search algorithm (CSA). A classical proportional-integral-derivative (PID) controller is also tuned using the CSA to examine the performance of the FLC. The effectiveness of the adaptive admittance control scheme is observed in the experimental results. Furthermore, the experimental results demonstrate that the optimized FLC significantly improves the tracking performance of the ankle rehabilitation robot and decreases the steady-state tracking errors about 50% compared to the optimized PID controller. The performances of the developed controllers are evaluated using common error based performance indices indicating that the FLC has roughly 50% better performance than the PID controller.     

Fuzzy sliding mode control for a robot manipulator

This work presents the design of a robust control system using a sliding mode controller that incorporates a fuzzy control scheme. The presented control law superposes a sliding mode controller and a fuzzy logic controller. A fuzzy tuning scheme is employed to improve the performance of the control system. The proposed fuzzy sliding mode control (FSMC) scheme utilizes the complementary cooperation of the traditional sliding mode control (SMC) and the fuzzy logic control (FLC). In other words, the proposed control scheme has the advantages which it can guarantee the stability in the sense of Lyapunov function theory and can ameliorate the tracking errors, compared with the FLC and SMC, respectively. Simulation results for the trajectory tracking control of a two-link robot manipulator are presented to show the feasibility and robustness of the proposed control scheme. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A robust fuzzy logic controller for robot manipulators withuncertainties

Owing to load variation and unmodeled dynamics, a robot manipulator can be classified as a nonlinear dynamic system with structured and unstructured uncertainties. In this paper, the stability and robustness of a class of the fuzzy logic control (FLC) is investigated and a robust FLC is proposed for a robot manipulator with uncertainties. In order to show the performance of the proposed control algorithm, computer simulations are carried out on a simple two-link robot manipulator.