基于Hammerstein-Wiener逆模型补偿的预测控制 非线性变换策略

针对一类Hammerstein-Wiener模型描述的非线性控制系统,提出一种基于逆模型补偿的预测控制策略.在控制优化计算中,利用Wiener非线性环节的逆模型分别对系统输出设定值和采样值进行变换;控制实施过程中,将控制器输出操作量经过Hammerstein静态非线性环节模型逆变换后施加到实际被控对象上,通过两次逆变换,使得标称模型下控制器输出与闭环系统中线性环节的输入相一致.通过非线性变换补偿将非线性过程的控制转化为线性系统控制,避免了对非线性模型进行优化计算量大及预测不准确的问题.最后通过仿真验证了所提方案的可行性及有效性.     

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Wiener模型的传感器动态非线性辨识研究

针对实际测量中传感器存在较大非线性的缺点,提出利用改进型Wiener模型描述传感器动态非线性模型;将Wiener模型的动态线性环节和静态非线性环节分别利用Laguerre函数和最小二乘支持向量机进行辨识,最终实现传感器模型的建立;通过仿真实验验证比较不同方法的辨识误差与速度,最终结果表明该方法在非线性动态传感器模型辨识方面具有明显的速度和精度优势。     

模型预测控制快速梯度优化算法评估北大核心CSCD

基于快速梯度法的一类在线优化算法因其可预估给定求解精度下的计算量上界而被用于实时模型预测控制的在线求解。由于各算法针对的控制问题形式不同及算法设计的差别,对于非线性模型预测控制问题,难以有效快速地选择合适的算法。首先,对各个算法的特性及适用范围进行了简要描述;然后,针对解决具有状态约束和输入约束的非线性模型预测控制问题,给出了各个算法在求解过程中的计算复杂度,通过非线性实例验证和比较了各算法的性能。研究结果可为选择合适的模型预测控制算法提供参考。     

基于混合神经网络的非线性预测函数控制

针对基本预测函数控制只能用于线性对象的控制这一不足,提出了基于混合神经网络的非线性预测函数控制.混合神经网络由BP网络和线性神经网络串连组成.采用混合神经网络对可用Hammerstein模型描述的非线性对象进行有效的辨识.其中,BP网络反映了非线性静态增益,线性神经网络反映了线性动态子系统.利用BP网络求出非线性静态增益的逆并与非线性对象串联,抵消非线性对象中的非线性静态增益部分,将非线性对象的控制问题转化为对线性对象的控制问题,实现了对非线性对象的预测函数控制.当被控对象的特性发生变化时,可对混合神经网络权值及时进行修正并调整控制器参数使控制系统始终保持良好的控制性能.仿真结果表明,此控制系统具有良好的控制效果.     

基于神经网络的非线性系统多步预测控制

针对离散非线性系统,利用非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络多步预测控制方法,并给出了控制律的收敛性分析．该方法将非线性系统处理成简单的线性和非线性两部分,对复杂的非线性多步预测方程给出了直观而有效的线性形式,并用线性预测控制方法求得控制律,避免了复杂的非线性优化求解．仿真结果表明了该算法的有效性．     

基于多面体终端域的Hammerstein-Wiener非线性预测控制

许多实际系统都可以表示成一种中间为线性动态子系统、输入输出端为非线性静态子系统的Hammerstein-Wiener型非线性模型. 针对输入和输出受约束的Hammerstein-Wiener型非线性系统, 提出一种基于多面体终端域的预测控制综合算法. 离线设计时, 通过构造一系列多面体不变集, 扩大了终端域; 在多面体不变集内, 设计非线性控制律, 减少了常规线性控制律设计的保守性. 在线计算时, 通过求解有限个线性矩阵不等式(Linear matrix inequalities, LMIs)优化问题, 不仅可以满足实时性要求, 而且能够改善控制性能. 仿真结果表明了采用多面体不变集的优越性.     

基于高斯混合飞蛾优化算法的Wiener模型辨识

针对Wiener模型辨识问题,结合函数连接型神经网络(FLANN)和飞蛾优化算法(MFO)的优势,提出了一种新型的辨识方案。利用FLANN来拟合静态非线性模块,通过将辨识问题转化为优化问题来对线性部分和非线性部分的参数同时进行更新。为了提升飞蛾优化算法的辨识性能,将高斯混合分布思想引入飞蛾种群初始化以及位置更新中,提出了一种新型的高斯混合飞蛾优化算法(GMFO),并通过测试函数验证了其寻优性能。最后通过仿真实验结果证明了所提出辨识方案的有效性和鲁棒性。     

Wiener型非线性系统的La-RBF组合模型预测控制

针对Wiener型非线性系统,本文提出了一种基于Laguerre函数模型与RBF神经网络模型的组合模型的预测控制策略,研究结果表明该组合模型兼具两者的优点,适用范围广,对系统变时延,变阶次及变非线性都具有良好的控制效构     

Application of Wiener model predictive control (WMPC) to an industrial C2-splitter

Dual composition control of a high-purity distillation column is recognized as an industrially important, yet notoriously difficult control problem. It is proposed, however, that Wiener models, consisting of a linear dynamic element followed in series by a static nonlinear element, are ideal for representing this and several other nonlinear processes. They are relatively simple models requiring little more effort in development than a standard linear step response model, yet offer superior characterization of systems with highly nonlinear gains. Wiener models may be incorporated into MPC schemes in a unique way that effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC, especially in the analysis of stability. In this paper, Wiener model predictive control is applied to an industrial C2-splitter at the Orica Olefines plant with promising results.     

Input-output linearizing control of constrained nonlinear processes

An input-output linearization strategy for constrained nonlinear processes is proposed. The system may have constraints on both the manipulated input and the controlled output. The nonlinear control system is comprised of: (i) an input-output linearizing controller that compensates for processes nonlinearities; (ii) a constraint mapping algorithm that transforms the original input constraints into constraints on the manipulated input of the feedback linearized system; (iii) a linear model predictive controller that regulates the resulting constrained linear system; and (iv) a disturbance model that ensures offset-free setpoint tracking. As a result of these features, the approach combines the computational simplicity of input output linearization and the constraint handling capability of model predictive control. Simulation results for a continuous stirred tank reactor demonstrate the superior performance of the proposed strategy as compared to conventional input-output linearizing control and model predictive control techniques.     

基于PSO滚动优化的LS-SVM预测控制*

针对非线性时延系统、传统预测控制算法难以建立精确模型、控制精度不高的现状,提出一种基于最小二乘支持向量机（LS-SVM）的非线性系统预测控制算法。该算法通过LS-SVM对非线性系统输入输出数据序列的训练学习,建立其预测模型;然后运用粒子群(PSO)算法完成非线性预测控制的滚动优化。仿真结果表明,基于该方法的非线性系统预测控制具有较好的控制效果。     

Nonlinear model predictive control of multivariable processes using block-structured models

Block-structured models, such as Wiener or Hammerstein models, have been used in nonlinear model predictive control to reduce the cost of identification and online computation. The solution of a nonlinear dynamic optimization problem has been avoided by inverting the nonlinear element and solving the resulting linear problem in the past. However, by exploiting the block structure for sensitivity calculation, the original nonlinear problem can also be solved at low computational cost. At the same time, greater modeling flexibility is achieved. Recently, a new Hammerstein model structure has been proposed for multivariable processes with input directionality, which exploits such increased modeling flexibility. This paper deals with nonlinear model predictive control constrained by models of Hammerstein or Uryson structure. A method for efficient calculation of sensitivity information is developed. In a simulation example, the method is shown to combine low computational cost with a significant reduction of the loss of optimality compared to the previous methods.     

Nonlinear predictive control of smooth nonlinear systems based on Volterra models. Application to a pilot plant

There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non‐autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real‐time control of a chemical pilot plant. Copyright © 2009 John Wiley & Sons, Ltd.     

一类双线性系统的广义预测控制研究

双线性模型预测控制的研究表明,采用一般双线性模型的预测控制将涉及非线性优化问题,在线处理相当困难,而采用线性近似模型的预测控制又会带来较大的偏差.针对一类输入一输出双线性系统,提出了一种双线性系统的广义预测控制算法.该算法将基于输入-输出模型双线性系统中的双线性项和线性项合并,建立了一种类似于线性系统的ARIMA模型,并充分利用多步最优预测信息,由递推近似实现多步预测.控制律具有解析形式,避免了一般非线性寻优的复杂计算,并能适用于非最小相位双线性系统.仿真实验表明该算法具有良好的控制效果.     

Bridging the gap between the linear and nonlinear predictive control: Adaptations for efficient building climate control

The linear model predictive control which is frequently used for building climate control benefits from the fact that the resulting optimization task is convex (thus easily and quickly solvable). On the other hand, the nonlinear model predictive control enables the use of a more detailed nonlinear model and it takes advantage of the fact that it addresses the optimization task more directly, however, it requires a more computationally complex algorithm for solving the non-convex optimization problem. In this paper, the gap between the linear and the nonlinear one is bridged by introducing a predictive controller with linear time-dependent model. Making use of linear time-dependent model of the building, the newly proposed controller obtains predictions which are closer to reality than those of linear time invariant model, however, the computational complexity is still kept low since the optimization task remains convex. The concept of linear time-dependent predictive controller is verified on a set of numerical experiments performed using a high fidelity model created in a building simulation environment and compared to the previously mentioned alternatives. Furthermore, the model for the nonlinear variant is identified using an adaptation of the existing model predictive control relevant identification method and the optimization algorithm for the nonlinear predictive controller is adapted such that it can handle also restrictions on discrete-valued nature of the manipulated variables. The presented comparisons show that the current adaptations lead to more efficient building climate control.     

一类不确定模糊模型的输出反馈鲁棒预测控制

针对一类满足扇形界条件的不确定模糊模型描述的非线性系统,提出一种输出反馈鲁棒预测控制方法．该方法将鲁棒预测控制的Min-Max优化问题转化为具有LMI约束的线性目标最小化问题,并且不需系统状态完全可测,仅仅利用系统测量输出和不可测状态的界限值来确定保证闭环系统鲁棒稳定的输出反馈控制器．仿真实验证明了该方法的有效性．