平行二级倒立摆的稳定控制

将单一输入规则群动态加权模糊推理模型应用于平行二级倒立摆系统，设计出具有6个输入交量的模糊稳定控制器。该模糊控制器规则总数少，直观性强。计算机仿真表明，该模糊控制器能在短时间内同时实现两摆角度及小车位置的控制。     

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。     

基于模糊满意度的广义预测控制器参数的在线调整

广义预测控制器（GPC）中控制量的加权系数λ直接影响着控制系统的调节品质，调整λ值对调节系统的超调量和调节时间的作用是相矛盾的，为此提出调节品质满意度的概念，形成基于模糊满意度的多目标决策问题，动态地调整GPC目标函数中的加权系数λ，仿真结果表明，该方法能够同时满足系统的多种控制品质的要求。     

多电机变频调速同步系统的多模型预测控制

针对多电机同步系统具有强烈的非线性和耦合性,难以建立精确数学模型并施加有效控制的缺陷,提出一种多电机多模型动态矩阵预测控制算法．在辨识不同工况子模型的基础上,设计各自的子控制器,并利用加权和形式构造全局控制器,在有约束的条件下,通过滚动优化的策略获得合适的控制增量．控制过程中,无需进行磁链观测．仿真结果验证了该算法的有效性．     

一类非线性系统的模糊变结构控制

研究了一类非线性系统的模糊变结构控制问题，并给出了稳定性证明。通过将非线性系统化为多个精确T—S模型来建立非线性系统精确的T—S模糊模型，将模糊理论与成熟的线性变结构控制理论相结合设计一种模糊变结构控制器，用Lyapunov稳定性理论证明该控制器能确保模糊动态模型全局渐近稳定，从而使非线性系统稳定。仿真结果表明了该设计方法的有效性。     

多电机变频调速同步系统的多模型预测控制

针对多电机同步系统具有强烈的非线性和耦合性,难以建立精确数学模型并施加有效控制的缺陷,提出一种多电机多模型动态矩阵预测控制算法．在辨识不同工况子模型的基础上,设计各自的子控制器,并利用加权和形式构造全局控制器,在有约束的条件下,通过滚动优化的策略获得合适的控制增量．控制过程中,无需进行磁链观测．仿真结果验证了该算法的有效性．

     

基于模糊动态模型的多变量系统模糊控制

采用模糊动态模型对多变量复杂非线性系统进行模糊控制.首先针对局部线性动态模 型设计状态反馈控制器,然后利用模糊推理确定整个系统的控制;在一系列局部模型通过模糊 隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并 利用大系统分散控制关联稳定性的概念和方法,得到了闭环模糊系统稳定的充分条件.仿真例 子验证了该设计方法的正确性.     

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．     

一类非线性系统的多模型预测控制

讨论了基于多模型的预测控制方法.对于化工生产过程中具有高度非线性的连续搅拌反应釜（CSTR）,通过对覆盖工况的数据离线辨识建立多个局部模型,根据每个局部模型分别设计子GPC控制器,通过跟踪工况变化对子控制器加权以获得控制增量.仿真结果表明该方法可取得令人满意的控制效果.     

机械手臂的多模型预测控制

针对机械手臂的非线性特点,提出了基于隶属度函数的多模型预测控制方法。该方法首先根据机械手臂的特点,选择合适的调度变量,将机械手臂的工作空间划分为若干个工作子空间,在每个子空间内的平衡点处对机械手臂进行线性化处理,得到相应的线性子模型,从而得到机械手臂的多模型表示;其次针对每个线性子模型设计局部预测控制器,使其在相应的子空间内达到控制要求;最后选择梯形隶属度函数与局部预测控制器进行加权求和,获得全局多模型预测控制器,以对机械手臂进行控制。仿真结果表明,当机械手臂的工作条件在大范围内变化时,全局多模型预测控制器的控制性能远优于常规PD控制器,达到了预期的控制目的。     

Decomposition property of fuzzy systems and its applications

This paper presents the decomposition property of fuzzy systems using a simple, constructive, decomposition procedure. That is, by properly dividing the input space into sub-input spaces, a general fuzzy system is decomposed into several sub-fuzzy systems which are the simplest fuzzy systems in the sub-input spaces. Based on the decomposition property of fuzzy systems, the analysis of fuzzy systems can be divided into two steps: first, analyze the properties of the simplest fuzzy systems, and then, use the decomposition property to extend the results to general fuzzy systems. Using this idea, two applications of the decomposition property are given. The first is the application to the representation capability analysis of fuzzy systems. The second is the application to the analysis of a class of nonlinear control systems. Then, based on the piecewise affine fuzzy-system model, the existence condition and the design of a stable control for a class of single-input single-output (SISO) nonlinear systems are presented     

Observers for Takagi-Sugeno fuzzy systems

We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system is then an accurate approximation of the original nonlinear system. Our approach to the analysis and design of observers for Takagi-Sugeno fuzzy systems is based on extending sliding mode observer schemes to the case of interpolated multiple local affine linear models. Thus, our main contribution is nonlinear observer analysis and design methods that can effectively deal with model/plant mismatches. Furthermore, we consider the difficult case when the weighting functions in the Takagi-Sugeno fuzzy system depend on the estimated state.     

一种基于T-S模糊模型的自适应预测函数控制

针对离散时间非线性系统，提出了一种基于T-S模糊模型的自适应预测函数控制算法。该算法利用加权递推最小二乘法在线辨识T-S模糊模型后件参数，以克服模型失配对系统性能的影响。根据辨识得到的模型参数直接递推计算模型的预测输出，而不需要求解Diophantine方程，进而直接递推求解预测控制律，而不需要求解矩阵逆。仿真结果表明，该算法具有良好的跟踪性能和较强的鲁棒性。     

智能化战术飞行轨迹规划方法研究

对态势评估与规划系统进行集成,提出了智能化战术飞行轨迹规划系统方案.基于贝叶斯网络和模糊推理技术,实现了战场威胁级别及其相对重要性程度的综合评估.利用模型预测控制的滚动优化和在线校正原理,实现了飞机在线飞行路径规划.建立了路径规划代价函数中加权因子的智能化分配方法,进而实现了威胁评估与路径规划之间的集成,使得路径规划系统能够自适应战场态势的动态变化.最后通过仿真验证了所提出方法的有效性.     

Guaranteed cost controller design for discrete-time switching fuzzy systems

With two level weighting functions, namely, crisp switching-region weighting functions and local fuzzy weighting functions, this paper introduces a discrete-time switching fuzzy system, which inherently contains the features of switched hybrid systems and Takagi-Sugeno (TS) fuzzy systems, and then, for this discrete-time switching fuzzy system, this paper proposes two new guaranteed cost state-feedback controllers minimizing an upper bound of state and input energy called LQ performance under all admissible grades of time-varying fuzzy weighting functions. The first one, associated with a piecewise quadratic Lyapunov function (PQLF), uses time-varying information on the switching-region weighting functions. The second one, associated with a new piece-wise fuzzy weighting-dependent Lyapunov function (PFWLF), uses time-varying information on the local fuzzy weighting functions as well as on the switching-region functions. Especially with a new special structure of the candidate of PFWLF, the PFWLF-based controller employs not only the current-time but also the one-step-past information on the time-varying local fuzzy weighting functions.     

Exponential stability and robust H∞ control of a class of discrete-time switched non-linear systems with time-varying delays via T-S fuzzy model

This paper deals with stability and robust H_∞ control of discrete-time switched non-linear systems with time-varying delays. The T-S fuzzy models are utilised to represent each sub-non-linear system. Thus, with two level functions, namely, crisp switching functions and local fuzzy weighting functions, we introduce a discrete-time switched fuzzy systems, which inherently contain the features of the switched hybrid systems and T-S fuzzy systems. Piecewise fuzzy weighting-dependent Lyapunov–Krasovskii functionals (PFLKFs) and average dwell-time approach are utilised in this paper for the exponentially stability analysis and controller design, and with free fuzzy weighting matrix scheme, switching control laws are obtained such that H_∞ performance is satisfied. The conditions of stability and the control laws are given in the form of linear matrix inequalities (LMIs) that are numerically feasible. The state decay estimate is explicitly given. A numerical example and the control of delayed single link robot arm with uncertain part are given to demonstrate the efficiency of the proposed method.     

Group decision making with fuzzy linguistic preference relations via cooperative games method

When we consider the weighting approach for group decision making with fuzzy linguistic preference relations, the groupment of experts has merely been studied. In this paper, a novel weighting approach on the basis of cooperative games method is developed. The group decision error matrix is built to reflect the deviations of all experts with given initial weighting vector. An iterative algorithm is designed to lower the sum of the decision error so that a final convergence result can be obtained. The advantage of the weighting algorithm is that it can consider the contribution of each expert and reduce the sum of decision error with increasing iteration numbers. Then an optimization model using triangular fuzzy numbers as alternatives’ weights is constructed, whose results are used to rank the alternatives. Finally, a numerical example of subjective evaluation of vehicle sound quality is considered to illustrate the feasibility and validity of the proposed weighting approach in the group decision making problem.     

永磁同步电机模型预测转矩控制模糊排序法研究

针对永磁同步电机(PMSM)模型预测转矩控制(MPTC)中成本函数权重系数难以设计和调节的问题, 以降低逆变器开关频率的多目标控制问题为例, 本文研究了基于排序法的模型预测转矩控制策略. 本文通过优先级的设计解决了排序过程中最优电压矢量解不唯一的问题. 考虑到控制目标重要性并不完全等同, 提出了一种带有缩放因子的排序优化方法, 利用缩放因子来调节控制目标的重要程度. 不同于连续变化的权重系数, 缩放因子的作用效果具有离散分段特性, 因此其调整过程可得到有效简化. 本文进一步提出了基于模糊排序法的模型预测转矩控制策略, 实现了缩放因子的动态优化, 从而更好地适应电机不断变化的运行状态. 仿真结果表明, 与固定权重系数的传统模型预测转矩控制相比, 本文所提控制策略可降低系统的平均开关频率、转矩与磁链脉动, 并可有效抑制动态条件下的转矩和磁链脉动. 实时性实验结果表明, 排序法不会严重降低模型预测转矩控制的实时性.     

非线性动态模糊系统过程控制与稳定性分析

针对自动控制领域中存在的大量的非线性动态模糊系统,提出了非线性动态模糊系统过程控制模型,并给出了动态模糊控制器的设计算法和该模型的稳定性分析,很好地解决了模糊控制系统所不能解决的动态性问题.