采用集结的预测控制器仿真研究

集结策略在减少预测控制在线计算量同时也必然带来控制性能的下降,研究集结策略与控制器性能的关系,对于应用集结策略有着重要的意义.约束预测控制器不存在公式解,无法从理论上得到集结策略与控制器性能的关系.通过仿真研究的方法,结合定性分析,研究了集结阵维数和集结阵结构对控制器性能影响,以及衰减策略下不同衰减率的集结阵对控制器性能影响.仿真得到的结果能有效地指导实际系统中集结阵的选取,使集结预测控制器具有良好的性能.     

预测控制优化变量的集结策略

为减少预测控制的在线计算量，提出了对优化变量进行集结的优化策略，通过一个集结矩阵将原优化变量与集结后的变量联系起来，从而为多种预测控制算法建立一个统一的框架，分析了现有的几种典型优化策略的集结表述，说明了所提出的集结优化框架具有一定的普适性，并在此框架下提出一种新的具有输出衰减形式的集结优化算法。     

基于线性集结的预测控制器

针对一类受约束的线性离散系统,研究了基于集结预测控制器的可行性问题．通过一个列满秩的集结矩阵将维数较小的控制变量序列映射成在线优化变量序列,在不缩短控制时域的情况下,降低了在线优化变量的个数．给出了优化变量可集结的充分条件,当集结矩阵满足这个条件时,在线求解优化问题总会得到可行解．最后,给出了一个仿真实例,仿真结果很好地验证了本文的结论．     

基于集结策略的非线性稳定预测控制器

针对有约束非线性系统预测控制在线计算量大的问题，引入集结策略降低其在线计算量并重点讨论了系统的稳定性问题．指出当控制器的终端状态处于某集合内且集结衰减系数的上界满足一定条件时，其最优目标函数递减．进而提出了一个双模控制律，可使系统渐近稳定．最后，通过仿真对该结论进行了验证．     

一种基于衰减集结的鲁棒预测控制器

高效鲁棒预测控制 (ERPC) 是一种在线计算量较小, 且控制性能较好的鲁棒预测控制算法. 但采用单一椭圆不变集的设计方法存在保守性. 本文采用衰减集结策略, 通过离线设计在系统状态空间中投影彼此正交的两个椭圆不变集, 在线进行凸组合的方法设计 ERPC 控制器, 使系统初始可行域进一步扩大, 并在一定程度上改善了控制性能.     

单机调度问题对偶集结迭代算法

具有到达时间约束、目标为最小化加权完工时间之和的单机调度问题是一个典型的NP-hard问题,采用时间下标建模的线性规划松弛方法可提供一个很强的下界,但优化求解存在维数困难.为此,本文提出了一种对偶集结优化策略,通过选择一个衰减集结矩阵集结对偶乘子变量,利用对偶理论获得模型的约束集结,从而降低计算复杂度.同时分析了集结模型的结构特性,并提出一种迭代算法来改善下界.仿真结果表明对偶集结迭代算法能够减少计算时间,同时改善下界性能,适用于大规模调度问题.     

随机线性系统模型的集结简化

讨论集结法在简化随机线性系统模型中的应用，它可将n维状态空间模型的主要特征集结简化到r维（r〈n）状态空间模型上，优化地再现原系统的基本性能和模裂数据。在简化模型中，动态系统集结矩阵选定为原系统矩阵特征值的子集。这种方法既适用于离散系统，也适用于连续系统。高阶系统动态模型的这种简化处理方法，不仅克服与避免了对这类系统进行仿真分析时，占用较大内存空间与耗费大量机时的缺陷，而且提高厂仿真与控制过程的稳定性和结果的准确性。     

基于高速收敛粒子群优化算法的非线性系统无模型预测控制

基于无模型控制、粒子群优化和预测控制的思想,提出一种新型非线性无模型预测控制器,并对该控制器的收敛性进行了分析.该控制器以带误差修正的泛模型为预测模型,以高速收敛的粒子群优化算法为滚动优化策略,不仅避免了非线性预测控制中复杂的矩阵求逆运算,而且提高了算法的收敛速度,增强了实时性.仿真研究表明了该控制器的有效性.     

基于混沌优化的非线性预测控制器

针对非线性系统的控制问题,本文将神经网络辨识、混沌优化和预测控制思想有机结合,提出了一种新型非线性预测控制器.该控制器以神经网络作为预测模型,混沌优化算法作为滚动优化策略,避免了非线性预测控制中复杂的梯度计算和矩阵求逆问题.另外在训练神经网络过程中,采用了带混沌机制的自适应学习率的BP算法,以提高神经网络的收敛能力和收敛速度.仿真研究说明了该非线性预测控制器的有效性及实时性.     

多变量动态矩阵控制算法研究

研究精馏塔的优化控制问题,由于PID控制精度差,多变量解耦控制理想完全解耦难以实现,预测控制存在模型失配,参数与工程指标联系不紧密的缺点,提出基于PID的多变量动态矩阵控制(DMC)策略.通过在DMC性能指标中引入预测输出误差项,利用PID指标改变传统DMC优化过程.上述算法兼具预测控制的解耦特性与PID控制参数工程意义明确的特点,加快了Shell原油精馏塔响应速度,消除了产品干点在设定值附近振荡,减少一个产品干点改变致使另一产品干点波动的现象.同时改进算法与传统预测控制器结构类似,便于广大工程人员设计实现.     

Quality guaranteed aggregation based model predictive control and stability analysis

The input aggregation strategy can reduce the online computational burden of the model predictive controller. But generally aggregation based MPC controller may lead to poor control quality. Therefore, a new concept, equivalent aggregation, is proposed to guarantee the control quality of aggregation based MPC. From the general framework of input linear aggregation, the design methods of equivalent aggregation are developed for unconstrained and terminal zero constrained MPC, which guarantee the actual control inputs exactly to be equal to that of the original MPC. For constrained MPC, quasi-equivalent aggregation strategies are also discussed, aiming to make the difference between the control inputs of aggregation based MPC and original MPC as small as possible. The stability conditions are given for the quasi-equivalent aggregation based MPC as well. Supported by the National Natural Science Foundation of China (Grant No. 60674041), and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20070248004)     

Predictive control based on local linear fuzzy models

This paper deals with predictive control based on fuzzy models. A novel algorithm (LOLIMOT) is proposed for the construction of Takagi-Sugeno fuzzy models. The rule consequents are optimized by a local orthogonal least-squares method that selects the significant regressors. The rule premises are optimized by a tree construction algorithm which partitions the input space in hyper-rectangles. A generalized predictive controller (GPC) and a dynamic matrix controller (DMC) are designed. Both controllers require the extraction of a linear model from the Takagi-Sugeno fuzzy model. For the GPC a new technique called local dynamic linearization is proposed that exploits the special structure of the local linear models. The DMC is based on the evaluation of a step response. The effectiveness of both the identification algorithm and the predictive controllers is shown by application to temperature control of an industrial-scale cross-flow heat exchanger.     

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．     

Multi-model predictive control of an industrial C3/C4 splitter

In this paper, the application of a linear predictive controller to an industrial distillation column that presents a nonlinear behavior is described. The system is represented by a set of linear approximating models, where each model corresponds to a possible operating point of the system. The control sequence computed by the control algorithm is based on a min–max optimization problem where the controller cost is minimized for the worst process model. The control algorithm makes use of a particular form of the state-space model, which preserves the structure of conventional model predictive control controllers that are based on the step response model. The performance of the proposed controller applied to an industrial system is illustrated with results of the real system at typical plant conditions with the controller performing as a regulator and as an output reference tracker.     

The Mimo Predictive Pid Controller Design

This paper is concerned with the design of Multi‐Inputs and Multi‐Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model‐based predictive controllers. A new PID control structure is defined which incorporates the prediction of future outputs and uses future set point. A method is proposed to calculate the optimal values of the PID gains from generalised predictive control results. A decentralized version of the predictive PID controllers is presented and the stability of the closed loop system is studied. Simulation studies demonstrate the superior performance of the proposed controller compared with a conventional PID controller. The results are also compared with generalised predictive control solutions.     

Some investigations on hybrid fuzzy IPD controllers for proportional and derivative kick suppression

Proportional and derivative kick i.e., a large change in control action of a proportional plus integral plus derivative (PID) controller due to a sudden change in reference set-point is generally undesired in process industry. Therefore, the structure of conventional parallel PID controller is modified to integral minus proportional derivative (I-PD) controller. In this paper, three hybrid fuzzy IPD controllers such as a fuzzy I-fuzzy PD (FI-FPD) controller and its hybrid combinations with its conventional counterpart such as fuzzy I-PD (FI-PD) and I-fuzzy PD (I-FPD) are presented in view of above industrial problem. These controllers are based upon the counterpart conventional I-PD controller and contains analytical formulae. Computer simulations are carried out to evaluate the performance of hybrid fuzzy controllers along with conventional I-PD and PID controllers for set-point tracking and disturbance rejection for an induction motor in closed loop using LabVIEW? environment. The gains of conventional and hybrid fuzzy controllers are tuned using genetic algorithm (GA) for minimum overshoot and settling time. It has been observed that hybrid fuzzy controllers along with the conventional I-PD controller significantly remove the kick from the control action in reference set-point tracking. However, in disturbance rejection, I-PD and FI-PD controllers fail to eliminate the kick from the control signal. In contrast, FI-FPD and I-FPD controllers considerably reduced spikes from the control action in disturbance rejection. Among the conventional and hybrid fuzzy IPD controllers, FI-FPD demonstrates much better set-point tracking and disturbance rejection response with spike free control action.     

反馈控制器降阶的约束与聚集方法

根据线性随机系统的包含原理,研究系统状态反馈和输出反馈控制器的降阶问题,给出了系统控制器降阶的约束和聚集两类条件。以一个18阶系统降阶反馈控制器的设计为例,说明该方法既简化了控制器的结构,又不失全阶控制器的主要性能。