一种基于T-S模型的模糊预测控制

提出了一种基于T-S模型的模糊预测控制策略。T-S模糊模型用来描述对象的非线性动态特性,通过当前的工况参数实时在线的修正每一时刻的阶跃响应模型参数,将模糊模型作为常规线性预测控制DMC方法的预测模型,从而把T-S模型对复杂的非线性系统的良好描述特性和预测控制的滚动优化算法相结合,来实现利用常规线性预测控制策略对非线性系统的有效控制,有效地解决了复杂工业过程的强非线性问题。pH中和过程的仿真结果表明其性能明显优于传统的PID控制器。     

基于T—S模糊模型的多变量预测控制

提出一种基于T-S模糊模型的多输入多输出预测控制策略.T-S模糊模型用于描述对象的非线性动态特性,模糊规则将非线性系统划分为多个局部子线性模型.为提高预测控制性能,采用多步线性化模型构成多步预报器,从而将预测控制中的非线性优化问题转化为一个线性二次寻优问题.串接贮槽液位控制系统的仿真结果表明,多步线性化模型预测控制性能优于单步线性化模型预测控制性能.     

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.     

基于自组织神经网络的非线性系统建模

针对基于T-S模糊模型的非线性系统建模问题,提出了一种基于自组织神经网络的新方法.在T-S模糊模型的建模中,目前常用的模糊C均值聚类算法存在迭代次数多,计算耗时的缺点.首先,利用竞争学习算法对输入空间进行聚类,基于此结果,借助于模糊C均值聚类算法进一步优化聚类结果,提取T-S模糊模型的规则前件隶属函数参数.然后,采用最小二乘法求得T-S模糊模型的规则后件参数,从而建立起非线性系统的T-S模糊模型.最后,仿真结果表明,该方法可以为模糊建模提供好的模型结构,并且有较高的计算效率和精度.     

T-S模型的窑炉压力无超调快速广义预测控制设计

窑压是玻璃窑炉运行过程中重要的被控指标之一,直接影响窑炉能耗、寿命及产品成品率,优化窑炉压力控制具有重要的经济意义。由于受到众多因素的影响,窑压具有典型的非线性特性,现有方法的控制效果还有很大的提升空间。本文针对窑压设计了一套新型无超调快速模糊广义预测控制方法(NFGPC),先利用"离线+在线"组合辨识方法得到窑压对象的T-S模型;然后基于该模型对系统进行分片线性化得到时变CARIMA模型,以设计窑压广义预测控制律;结合柔化理论与新型滚动优化目标函数设计一种广义预测控制律,该方法无需求解逆矩阵即可得到控制输出,计算量更小;由于新目标函数的应用,该方法还能够克服传统GPC引起的超调效应。仿真结果表明该方法能够很好的处理窑压非线性系统建模问题,与PID控制、线性GPC(LGPC)以及模糊广义预测控制(FGPC)以及快速模糊广义预测控制(FFGPC)等方法控制效果的对比表明,NFFGPC在处理非线性系统控制问题上具有一定的优越性。     

扩展T-S模糊模型的PSO神经网络优化算法

针对机械设备具有模糊性和非线性的特点,提出了一种利用扩展T-S模糊模型的,自适应PSO算法和BP神经网络相结合的新型智能结构优化算法。通过自适应的高斯函数来更改基本T-S模糊模型中的隶属度函数,进而使用扩展的T-S模糊模型来调整PSO算法的参数。以BP 神经网络隐含层神经元数目为设计变量,提取训练后的均方误差作为评价函数,用改进后的粒子群算法进行寻优。把优化后的网络模型应用于轮盘结构优化中,实验表明,该方法在保证轮盘性能的同时,对其结构进行了重新优化,是一种可行的结构优化方法。     

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。     

非线性时滞系统双阶段神经网络的改进广义预测控制

针对一类工业控制系统中存在的非线性、大时滞等情况,提出一种基于双阶段神经网络的改进隐式广义预测控制方法。首先,设计了一种基于快速回归算法和蝙蝠算法的双阶段神经网络模型,用于对非线性时滞系统进行建模,避免非线性系统下的模型失配问题;其次,采用比例积分(proportional integration, PI)结构优化广义预测控制目标函数设计,提高隐式广义预测控制性能;同时,改进控制增量选取策略,利用所预测的未来控制增量修正当前时刻控制增量;最后,将所设计的预测模型和预测控制方法应用于一个数值案例和锅炉燃烧系统,验证了所提控制策略的有效性。     

模糊预测控制在pH中和过程中的应用

针对pH中和过程,提出了一种基于T-S模型的模糊预测控制算法,以实现系统的滚动优化控制。T-S模糊模型的前件和后件参数分别采用模糊C均值聚类(FCM)和正交最小二乘法(OLS)进行离线或在线辨识。在每一个采样时刻以当前辨识出的T-S模型为基础实现系统的局部动态线性化,再根据线性化模型对pH过程实施广义预测控制(GPC),得到当前的控制量。仿真表明了该控制方法具有较小的超调性质,且在扰动作用下能快速跟踪到设定值,具有很强的鲁棒性。     

基于T-S模糊模型的混沌系统脉冲控制

给出了一种基于T-S模糊模型的混沌系统模糊脉冲控制方法.首先给出了基于T-S模糊模型对非线性系统精确建模的原理,得到与混沌系统等价的T-S模糊系统.然后根据建模得到的T-S模糊系统,采用模糊脉冲控制技术来实现控制.最后,以控制Ndolschi混沌系统为例,证明了这种方法的有效性.     

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．     

Convenient T-S fuzzy model with enhanced performance using a novel swarm intelligent fuzzy clustering technique

To automatically extract T-S fuzzy models with enhanced performance from data is an interesting and important issue for fuzzy system modeling. In this paper, a novel methodology is proposed for this issue based on a three-step procedure. Firstly, the idea of variable length genotypes is introduced to the artificial bee colony (ABC) algorithm to derive a so-called Variable string length Artificial Bee Colony (VABC) algorithm. The VABC algorithm can be used to solve a kind of optimization problems where the length of the optimal solutions is not known as a priori. Secondly, fuzzy clustering without knowing cluster number as a priori is viewed as such kind of optimization problem. Thus, a novel version of Fuzzy C-Means clustering technique (VABC-FCM), holding powerful global search ability, is proposed based on the VABC algorithm. Use of VABC allows the encoding of variable cluster number. This makes VABC-FCM not require a priori specification of the cluster number. Finally, the proposed VABC-FCM algorithm is used to extract T-S fuzzy model from data. Such VABC-FCM based convenient T-S fuzzy model extraction methodology does not require a specification of rule number as a priori. Some artificial data sets are applied to validate the performance of the convenient T-S fuzzy model. The experimental results show that the proposed convenient T-S fuzzy model has low approximation error and high prediction accuracy with appreciate rule number. Moreover, the convenient T-S fuzzy model is used to model the characteristics of superheated steam temperature in power plant, and the results suggest the powerful performance of the proposed method.     

Distributed Proportional-spatial Derivative control of nonlinear parabolic systems via fuzzy PDE modeling approach

In this paper, a distributed fuzzy control design based on Proportional-spatial Derivative (P-sD) is proposed for the exponential stabilization of a class of nonlinear spatially distributed systems described by parabolic partial differential equations (PDEs). Initially, a Takagi-Sugeno (T-S) fuzzy parabolic PDE model is proposed to accurately represent the nonlinear parabolic PDE system. Then, based on the T-S fuzzy PDE model, a novel distributed fuzzy P-sD state feedback controller is developed by combining the PDE theory and the Lyapunov technique, such that the closed-loop PDE system is exponentially stable with a given decay rate. The sufficient condition on the existence of an exponentially stabilizing fuzzy controller is given in terms of a set of spatial differential linear matrix inequalities (SDLMIs). A recursive algorithm based on the finite-difference approximation and the linear matrix inequality (LMI) techniques is also provided to solve these SDLMIs. Finally, the developed design methodology is successfully applied to the feedback control of the Fitz-Hugh-Nagumo equation.     

Takagi-sugeno fuzzy model identification using coevolution particle swarm optimization with multi-strategy

The particle swarm optimization (PSO) algorithm is widely used in identifying Takagi-Sugeno (T-S) fuzzy system models. However, PSO suffers from premature convergence and is easily trapped into local optima, which affects the accuracy of T-S model identification. An immune coevolution particle swarm optimization with multi-strategy (ICPSO-MS) is proposed for modeling T-S fuzzy systems. The proposed ICPSO-MS consists of one elite subswarm and several normal subswarms. Each normal subswarm adopts a different strategy for adjusting the acceleration coefficients. A Cauchy learning operator is used to accelerate the convergence of the normal subswarm. During the iteration step, the best individual in each normal subswarm is added to the elite subswarm. Using adaptive hyper-mutation, the immune clonal selection operator is used to optimize the elite subswarm while the individuals in the elite subswarm migrate to the normal subswarms. This shared migration mechanism allows full exchange of information and coevolution. The performance of the proposed algorithm is evaluated on a suite of numerical optimization functions. The results show good performance of ICPSO-MS in solving numerical problems when compared with other recent variants of PSO. The performance of ICPSO-MS is further evaluated when identifying the T-S model, with simulation results on several typical nonlinear systems showing that the proposed method generates a good T-S fuzzy model with high accuracy and strong generalizability.     

Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control

Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints, large delay time, fuzziness, and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In recent years, T-S fuzzy modeling has been found to be an effective approach in performing predictive control. Intelligent optimization algorithms, such as chaos optimization algorithm (COA) and particle swarm optimization (PSO), have been shown to have faster convergence and higher iterative accuracy than those based on conventional optimization methods. In this paper, chaos particle swarm optimization (CPSO), which involves combining the strengths of COA and PSO, and T-S fuzzy modeling are proposed as approaches to perform constrained predictive control. Predictive control of temperature of continued hyperthermic celiac perfusion for medical treatment based on the proposed approaches was carried out. Simulation tests were conducted to evaluate the performance of temperature control based on T-S fuzzy modeling and CPSO. Test results indicate that the T-S fuzzy model based on CPSO outperforms models based on generalized predictive control, COA, and PSO.     

Finite-dimensional constrained fuzzy control for a class of nonlinear distributed process systems.

This correspondence studies the problem of finite-dimensional constrained fuzzy control for a class of systems described by nonlinear parabolic partial differential equations (PDEs). Initially, Galerkin's method is applied to the PDE system to derive a nonlinear ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, a systematic modeling procedure is given to construct exactly a Takagi-Sugeno (T-S) fuzzy model for the finite-dimensional ODE system under state constraints. Then, based on the T-S fuzzy model, a sufficient condition for the existence of a stabilizing fuzzy controller is derived, which guarantees that the state constraints are satisfied and provides an upper bound on the quadratic performance function for the finite-dimensional slow system. The resulting fuzzy controllers can also guarantee the exponential stability of the closed-loop PDE system. Moreover, a local optimization algorithm based on the linear matrix inequalities is proposed to compute the feedback gain matrices of a suboptimal fuzzy controller in the sense of minimizing the quadratic performance bound. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.     

蚁群聚类算法的T-S模糊模型辨识

基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。