基于T- S模型的钻杆对中自适应预测控制

为非线性系统建立T－S模糊模型,采用基于T－S模型的局部递推最小二乘法对模糊规则后件参数进行辨识，然后用一步预测控制的方法进行控制。在钻杆对中控制中，定义了矢量化的时间为控制量，采用上述方法，在实际中取得了良好的效果。     

基于T-S模糊模型的状态反馈预测控制

将T-S模糊模型和状态反馈预测控制相结合,提出了一种基于T-S模糊模型的预测控制算法.该算法把T-S模糊模型作为预测模型得到状态和输出的预估值,并利用可测的过程变量对输出预估值进行反馈修正,然后利用最优控制理论,由修正后的预估值和给定值计算出控制整个系统的控制律.本文还对串级CSTR控制系统的不同的初态、设定值及干扰情况下进行了仿真,仿真结果表明了该方法的有效性和可行性.     

基于T-S模糊模型的非线性预测控制策略

提出了一种新的基于T-S模糊模型的非线性预测控制策略. T-S模糊模型用于描述对象的非线性动态特性, 通过将模糊模型的输出反馈回来作为模型输入, 从而构成了模糊多步预报器. 由于T-S模糊模型每条规则的结论部分是一个线性模型, 因此整个模糊模型可以看作一个线性时变系统, 从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题, 以方便求解. pH中和过程的仿真结果表明其性能优于传统的动态矩阵控制器.     

基于特征选择和协同模糊聚类的模糊建模研究

为了提高模糊模型辨识效率,提出了一种新的模糊模型建摸方法,该方法由两步组成：（1）采用基于特征相似性的特征选择方法,去除原始数据的冗余;（2）利用协同模糊聚类与G-K相结合的算法初始化模糊模型,使其前件和后件参数得到优化。采用该算法对有效的特征进行协同模糊聚类,模型参数得到改善,提高了模糊模型辨识的效率。模糊建模的实验结果表明了该方法的有效性。     

基于鲁棒开环解耦的系统逆二自由度控制

针对MIMO系统的轨迹跟踪,提出一种基于鲁棒开环解耦的系统逆二自由度（2DOF）控制方法．该方法通过鲁棒开环解耦解决MIMO系统的耦合问题,利用系统逆2DOF控制保证轨迹跟踪性能．首先提出一种保证预补偿器阶数最小和解耦系统鲁棒性的鲁棒开环解耦方法;然后结合鲁棒开环解耦和系统逆2DOF控制,给出系统逆前馈控制和H∞混合灵敏度鲁棒反馈控制器的设计方法;最后通过设计实例及仿真结果验证了该控制方法的有效性．     

基于特征模型的柔性结构直接自适应模糊预测控制

为了避免用模态截断方法设计控制器时所引起的控制和观测溢出问题,把柔性结构模型转化为用含有未知系数的二阶差分方程描述的特征模型,对此模型提出了当柔性结构实现位置保持时的直接自适应模糊预测控制方法.此方法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.文中证明了所设计的控制器可使广义误差估计值收敛到原点的一个小邻域内.仿真结果验证了此方法的有效性.     

基于模糊树模型的自适应模糊滑模控制方法

本文针对单输入–单输出仿射非线性系统提出了一种基于模糊树模型的具有监督控制器的模糊滑模控制方法. 该方法用模糊树模型逼近非线性系统中的未知非线性函数, 得到初始的控制器, 然后在线调节模糊树模型中的线性参数, 改善控制器的性能, 实现对有界参考输入信号的跟踪控制. 模糊树辨识方法自适应划分输入空间, 大大减少模糊规则的数目, 在一定程度上可以缓解困扰模糊控制中的”规则爆炸”问题. 该方法通过监督控制器保证闭环系统所有信号有界. 通过理论分析, 证明了跟踪误差收敛到零. 用倒立摆进行仿真验证, 结果表明该方法用较少的模糊规则, 就能得到满意的控制效果, 有推广应用价值.     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

Internal model based robust inversion feedforward and feedback 2DOF control for LPV system with disturbance

To reduce the adverse effects on the control performance and disturbance rejection caused by system uncertainty, a novel internal model based robust inversion feedforward and feedback 2DOF control approach was proposed for LPV system with disturbance. The proposed control approach combines the internal model control and robust inversion based 2DOF control, it utilizes internal model based control to reject external disturbance, utilizes robust inversion 2DOF control to enhance the control resolution and guarantee the system control performance. At first, a LMI synthesis approach for LPV system model identification and a disturbance compensator optimization design method which could minimize H_∞ norm of output error caused by disturbance are presented. Then, combined with internal loop for disturbance compensation, a robust inversion feedforward controller is designed by robust inversion approach and the feedback controller which could render the requirements of reference signal tracking performance and robustness satisfied is obtained by the H_∞ mixed sensitivity synthesis approach. Finally, atomic force microscopy (AFM) vertical positioning simulation experiments are conducted and the experiment results showed that the proposed control approach could achieve better output performance and disturbance rejection compared with conventional internal model based control and robust inversion based 2DOF control approach.     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

基于T-S 模型的模糊预测控制研究

提出一种基于T—S模型的模糊预测控制策略．利用模糊聚类算法高线辨识T—S模型，采用带遗忘因子的递推最小二乘法进行模型参数的选择性在线学习；对模糊模型在每一采样点进行线性化，将T—S模型表示的非线性系统转化为线性时变状态空间模型，并将约束非线性优化问题转化为线性二次规划问题，解决了非线性预测控制中如何获得非线性模型和非线性优化在线求解的难题．将预测域内的线性模型序列作为预测模型，减小了模型误差，提高了控制性能．pH中和过程的仿真验证了该方法的有效性．     

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.     

A robust least squares based approach to min‐max model predictive control

This article deals with the model predictive control (MPC) of linear, time‐invariant discrete‐time polytopic (LTIDP) systems. The 2‐fold aim is to simplify the treatment of complex issues like stability and feasibility analysis of MPC in the presence of parametric uncertainty as well as to reduce the complexity of the relative optimization procedure. The new approach is based on a two degrees of freedom (2DOF) control scheme, where the output r(k) of the feedforward input estimator (IE) is used as input forcing the closed‐loop system ∑_f. ∑_f is the feedback connection of an LTIDP plant ∑_p with an LTI feedback controller ∑_g. Both cases of plants with measurable and unmeasurable state are considered. The task of ∑_g is to guarantee the quadratic stability of ∑_f, as well as the fulfillment of hard constraints on some physical variables for any input r(k) satisfying an “a priori” determined admissibility condition. The input r(k) is computed by the feedforward IE through the on‐line minimization of a worst‐case finite‐horizon quadratic cost functional and is applied to ∑_f according to the usual receding horizon strategy. The on‐line constrained optimization problem is here simplified, reducing the number of the involved constraints and decision variables. This is obtained modeling r(k) as a B‐spline function, which is known to admit a parsimonious parametric representation. This allows us to reformulate the minimization of the worst‐case cost functional as a box‐constrained robust least squares estimation problem, which can be efficiently solved using second‐order cone programming.     

基于Sugeno 模糊模型的帆船控制方法研究

针对非线性、时变的帆船航行系统，提出一种基于Sugeno模糊模型的帆船控制新方法．采集舵手的航行经验建立知识库，将专家知识融合到控制系统中，提高了控制系统的智能度；采用Sugeno模糊模型，将舵角的非线性控制局部线性化，并设计相应的局部线性控制器，通过模糊推理综合各局部线性控制器的输出，得到全局控制量．仿真结果表明，该方法能实现对帆船航向的智能控制，具有一定的实用价值．     

基于AR模型的动态模糊聚类算法

与传统的硬划分聚类相比,模糊聚类算法(以FCM为例)对数据的比例变化具有鲁棒性,能够更准确地反映数据点与类中心的实际关系,目前已得到广泛应用.然而对于时序基因表达数据来说,传统的聚类算法往往不能充分利用到数据中时间上的动态关联信息.因此可以在模糊聚类算法的基础上引入自回归(AR)模型,将时序基因表达数据作为一组时间序列进行动态的聚类分析.这样不仅可以充分利用到时序基因表达数据的内部自相关性,并且可以进一步利用隶属度函数对AR模型的预测过程进行模糊化调整,从而得到更为理想的聚类结果.     

Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

Effective model is a novel tool for decentralized controller design to handle the interconnected interactions in a multi-input-multi-output (MIMO) process. In this paper, Type-1 and Type-2 effective Takagi-Sugeno fuzzy models (ETSM) are investigated. By means of the loop pairing criterion, simple calculations are given to build Type-1/Type-2 ETSMs which are used to describe a group of non-interacting equivalent single-input-single-output (SISO) systems to represent an MIMO process, consequently the decentralized controller design can be converted to multiple independent single-loop controller designs, and enjoy the well-developed linear control algorithms. The main contributions of this paper are: i) Compared to the existing T-S fuzzy model based decentralized control methods using extra terms to characterize interactions, ETSM is a simple feasible alternative; ii) Compared to the existing effective model methods using linear transfer functions, ETSM can be carried out without requiring exact mathematical process functions, and lays a basis to develop robust controllers since fuzzy system is powerful to handle uncertainties; iii) Type-1 and Type-2 ETSMs are presented under a unified framework to provide objective comparisons. A nonlinear MIMO process is used to demonstrate the ETSMs’ superiority over the effective transfer function (ETF) counterparts as well as the evident advantage of Type-2 ETSMs in terms of robustness. A multi-evaporator refrigeration system is employed to validate the practicability of the proposed methods.     

Adaptive fuzzy dynamic surface control for uncertain discrete-time non-linear pure-feedback MIMO systems with network-induced time-delay based on state observer

In this paper, a state observer-based adaptive fuzzy dynamic surface control is developed for uncertain discrete-time non-linear pure-feedback multiple-input-multiple-output (MIMO) systems with network-induced time-delay. The uncertainties are approximated by a set of adaptive fuzzy logic systems, with the adjusted parameters updated by a simplified recursive least squares estimation algorithm, combined with a state observer. For a constant known network-induced time-delay, the proposed modified dynamic surface control utilising the predicted system states, expands the acceptable network-induced time-delay and stable operating range for a discrete-time non-linear pure-feedback MIMO system in the network. The simulation results indicate that the presented method is effective.     

Output regulation of nonlinear singularly perturbed systems based on T-S fuzzy model

1IntroductionNonlinear singularly perturbed systems arise in a widevariety of engineering applications,representative examplesinclude catalytic continuous stirred_tank reactors[1],biochemical reactors[2],fluidized catalytic crackers[3],flexible mechanical systems[4],electromechanical networks[5],etc.For such systems,the output regulation problem,i.e.the problem of having the output tracking reference(or rejecting disturbance)signals produced by someexternal generator,is ofimportance.In linear …     

Output regulation of nonlinear singularly perturbed systems based on T-S fuzzy model

Based on the T_S model,the output regulation of nonlinear singularly perturbed systems via state feedback is discussed.It is shown that,under standard assumptions,this problem is solvable if and only if certain linear matrix equations are solvable.Once these equations are solvable, the state feedback regulator can easily be constructed.     

A survey on industrial applications of fuzzy control

Fuzzy control has long been applied to industry with several important theoretical results and successful results. Originally introduced as model-free control design approach, model-based fuzzy control has gained widespread significance in the past decade. This paper presents a survey on recent developments of analysis and design of fuzzy control systems focused on industrial applications reported after 2000.