首页 | 官方网站   微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 109 毫秒
1.
基于L S-SVM 的非线性预测控制技术   总被引:22,自引:1,他引:22       下载免费PDF全文
探讨了利用最小二乘支持向量机(LS—SVM)进行非线性系统辨识的方法,LS—SVM用等式约束代替传统支持向量机中不等式约束,求解过程从解QP问题变成解一组等式方程,将得到的LS—SVM模型应用到非线性预测控制,提出了基于LS—SVM模型的非线性预测控制算法,通过CSTR过程仿真表明,最小二乘支持向量机学习速度快,在小样本情况下具有良好的非线性建模和泛化能力,基于LS—SVM的预测控制算法具有很好的控制性能。  相似文献   

2.
提出一类非线性系统基于最小二乘支持向量机的直接自适应控制方法.该方法采用最小二乘支持向量机构造自适应控制器,自适应控制器参数的在线调整规律由Lyapunov稳定性理论导出,并严格证明了闭环系统的渐近稳定性.仿真研究表明了此控制方案的可行性和有效性.  相似文献   

3.
提出了一种基于最小二乘支持向量机(LS-SVM)的一类不确定非自治系统自适应控制器设计方法.该方法基于最小二乘支持向量机来估计对象的部分未知非线性项,并给出了最小二乘支持向量机权向量和偏移值的在线学习规则.利用李亚普诺夫理论严格证明了整个闭环系统的跟踪误差、控制器参数以及最小二乘支持向量机权参数和偏移值的一致最终有界.此控制方法可以保证对象在线稳定地跟踪任何光滑的目标轨迹,仿真结果表明了此控制方法的可行性和有效性.  相似文献   

4.
针对状态不可测的单输入单输出非线性不确定系统,提出一种基于最小二乘支持向量机(LS-SVM)的直接自适应输出反馈控制方法.该方法首先设计一种误差观测器,间接地估计出系统的状态,然后采用最小二乘支持向量机构造自适应控制器,控制器参数的在线调整规律由李亚普诺夫稳定性理论导出.文中严格证明了闭环系统的渐近稳定性,仿真研究表明了此控制方法的可行性和有效性.  相似文献   

5.
提出一类非线性不确定系统基于最小二乘支持向量机的白适应H∞控制方法.该方法基于最小二乘支持向量机估计对象的未知非线性函数,并给出了最小二乘支持向量机权向量和偏移值的在线学习规则.引入H∞控制器用于减弱外部干扰及最小二乘支持向量机近似误差对输出误差的影响.利用李亚普诺夫理论证明了整个闭环系统一致最终有界稳定.仿真结果表明了该方法的有效性.  相似文献   

6.
基于LS-SVM的小样本费用智能预测   总被引:5,自引:3,他引:5  
最小二乘支持向量机引入最小二乘线性系统到支持向量机中,代替传统的支持向量机采用二次规划方法解决函数估计问题。该文推导了用于函数估计的最小二乘支持向量机算法,构建了基于最小二乘支持向量机的智能预测模型,并对机载电子设备费用预测进行了研究。结果表明最小二乘支持向量机具有比多元对数回归更高的小样本费用预测精度。  相似文献   

7.
提出了一种基于最小二乘支持向量机的非线性系统鲁棒自适应跟踪控制方法. 该方法通过最小二乘支 持向量机在线修正由于建模误差、不确定因素等引起的非线性系统逆误差,使得系统输出可以准确跟踪参考模型输 出. 最小二乘支持向量机的参数调整规律由李亚普诺夫稳定性理论导出. 文中证明了非线性系统的稳定性,仿真结果 证明了该方法的有效性.  相似文献   

8.
潘正强  周经伦  郑龙 《计算机仿真》2007,24(4):170-171,179
针对实际工业过程中的非线性及时变特性,传统预测控制算法就难于建立精确的数学模型,从而提出了一种基于最小二乘支持向量机预报的动态矩阵预测控制模型.在整个过程中,首先建立基于最小二乘支持向量机的非线性动态矩阵预测控制结构,通过利用最小二乘支持向量机辨识被控对象模型,同时预测对象的未来输出,然后用动态矩阵控制算法进行滚动优化和反馈校正.仿真实例表明该模型对预测结果有很好的控制作用,有效消除输入干扰的影响,从而提高了预测精度.  相似文献   

9.
针对木材干燥系统强耦合非线性的特性,提出了一种基于小波最小二乘支持向量机的预测控制方法.讨论了利用小波支持向量机对木材干燥系统进行系统识别的方法,并将辨识模型应用于预测控制算法,实现了木材干燥的自适应控制.仿真结果表明,基于小波支持向量机的预测控制技术具有较好的鲁棒性,对木材干燥系统有很好的实用性.  相似文献   

10.
为了提高企业财务困境预测的正确率,减少训练模型的样本数和训练时间,在传统支持向量机预测模型的基础上,将Renyi熵和最小二乘支持向量机算法应用于财务困境预测,提出了一种基于Renyi熵的最小二乘支持向量机预测模型.独立推导出了适合财务困境预测这一离散序列的熵以及支持向量机核函数的表达式,同时,给出了这一改进算法的实现步骤.实验结果表明,该算法无论是训练样本的数量还是训练时间,都显著优于传统的最小二乘支持向量机以及标准支持向量机预测模型.  相似文献   

11.
A method is proposed for on-line reconfiguration of the terminal constraint used to provide theoretical nominal stability guarantees in linear model predictive control (MPC). By parameterising the terminal constraint, its complete reconstruction is avoided when input constraints are modified to accommodate faults. To enlarge the region of feasibility of the terminal control law for a certain class of input faults with redundantly actuated plants, the linear terminal controller is defined in terms of virtual commands. A suitable terminal cost weighting for the reconfigurable MPC is obtained by means of an upper bound on the cost for all feasible realisations of the virtual commands from the terminal controller. Conditions are proposed that guarantee feasibility recovery for a defined subset of faults. The proposed method is demonstrated by means of a numerical example.  相似文献   

12.
On the stability of constrained MPC without terminal constraint   总被引:2,自引:0,他引:2  
The usual way to guarantee stability of model predictive control (MPC) strategies is based on a terminal cost function and a terminal constraint region. This note analyzes the stability of MPC when the terminal constraint is removed. This is particularly interesting when the system is unconstrained on the state. In this case, the computational burden of the optimization problem does not have to be increased by introducing terminal state constraints due to stabilizing reasons. A region in which the terminal constraint can be removed from the optimization problem is characterized depending on some of the design parameters of MPC. This region is a domain of attraction of the MPC without terminal constraint. Based on this result, it is proved that weighting the terminal cost, this domain of attraction of the MPC controller without terminal constraint is enlarged reaching (practically) the same domain of attraction of the MPC with terminal constraint; moreover, a practical procedure to calculate the stabilizing weighting factor for a given initial state is shown. Finally, these results are extended to the case of suboptimal solutions and an asymptotically stabilizing suboptimal controller without terminal constraint is presented.  相似文献   

13.
In the standard model predictive control implementation, first a steady-state optimization yields the equilibrium point with minimal economic cost. Then, the deviation from the computed best steady state is chosen as the stage cost for the dynamic regulation problem. The computed best equilibrium point may not be the global minimum of the economic cost, and hence, choosing the economic cost as the stage cost for the dynamic regulation problem, rather than the deviation from the best steady state, offers potential for improving the economic performance of the system. It has been previously shown that the existing framework for MPC stability analysis, which addresses to the standard class of problems with a regulation objective, does not extend to economic MPC. Previous work on economic MPC developed new tools for stability analysis and identified sufficient conditions for asymptotic stability. These tools were developed for the terminal constraint MPC formulation, in which the system is stabilized by forcing the state to the best equilibrium point at the end of the horizon. In this work, we relax this constraint by imposing a region constraint on the terminal state instead of a point constraint, and adding a penalty on the terminal state to the regulator cost. We extend the stability analysis tools, developed for terminal constraint economic MPC, to the proposed formulation and establish that strict dissipativity is sufficient for guaranteeing asymptotic stability of the closed-loop system. We also show that the average closed-loop performance outperforms the best steady-state performance. For implementing the proposed formulation, a rigorous analysis for computing the appropriate terminal penalty and the terminal region is presented. A further extension, in which the terminal constraint is completely removed by modifying the regulator cost function, is also presented along with its stability analysis. Finally, an illustrative example is presented to demonstrate the differences between the terminal constraint and the proposed terminal penalty formulation.  相似文献   

14.
This paper briefly reviews development of nonlinear model predictive control (NMPC) schemes for finite horizon prediction and basic computational algorithms that can solve the stable real‐time implementation of NMPC in space state form with state and input constraints. In order to ensure stability within a finite prediction horizon, most NMPC schemes use a terminal region constraint at the end of the prediction horizon — a particular NMPC scheme using a terminal region constraint, namely quasi‐infinite horizon, that guarantees asymptotic closed‐loop stability with input constraints is presented. However, when nonlinear processes have both input and state constraints, difficulty arises from failure to satisfy the state constraints due to constraints on input. Therefore, a new NMPC scheme without a terminal region constraint is developed using soften state constraints. A brief comparative simulation study of two NMPC schemes: quasi‐infinite horizon and soften state constraints is done via simple nonlinear examples to demonstrate the ability of the soften state constraints scheme. Finally, some features of future research from this study are discussed.  相似文献   

15.
研究分段线性(PL)系统预测控制问题,提出了PL系统双模预测控制,并证明了该方法的稳定性.该方法使用混合逻辑动态系统来建模PL系统,利用PL系统状态反馈控制来确定PL系统的受控不变集,并结合双模预测控制方法获得PL系统双模预测控制.该方法解决了系数矩阵的选择问题,不需要满足最终状态等式约束.一个分段线性系统的实例证明了该方法是可行的.  相似文献   

16.
设计了一种基于可达集的鲁棒模型预测控制算法.首先确定了一个鲁棒不变集,并将此不变集用作模型预测控制的终端约束集;接着采用终端约束集对可达集的包含度作为优化指标;最后,采用预测时域逐渐减小的控制策略以保证在线优化存在可行解.从理论上证明了吸引域内的任意点在有限时域内都会被引导至终端约束集并始终停留在此集之内,并由仿真算例验证了本文所设计鲁棒模型预测控制算法的可行性.  相似文献   

17.
于洋  许鋆  罗雄麟 《自动化学报》2014,40(9):1922-1932
约束预测控制(Constrained model predictive control,CMPC)中,因约束的存在,优化过程中最优控制作用可能会在可行域的边界取值,也就是说会 有一个或多个变量饱和,即约束边界效应. 而过程控制中操纵变量饱和是我们不希望出现的. 对此,首先基于稳态模型,对期望值位于可行域内时最优解必在期望值处达到给出证明;同时证明了期望值在可行域外时最优解可转化为期望值到可行域的投影. 其次,针对变量在动态及稳态过程中饱和的情况提出了改善控制性能的措施——调整目标函数;终端约束的加入,为预测控制系统稳定性提供了保障. 通过对包含约束的连续搅拌釜式反应器(Continuous stirred tank reactor,CSTR)系统进行仿真实验,验证了所提方法的正确性,并说明了对目标函数进行适当调整,可有效改善系统的控制性能.  相似文献   

18.
A design of adaptive model predictive control (MPC) based on adaptive control Lyapunov function (aCLF) is proposed in this article for nonlinear continuous systems with part of its dynamics being unknown at the starting time. Specifically, to guarantee the convergence of the closed-loop system with online predictive model updating, a stability constraint is designed. It limits the aCLF of the system under the MPC to be less than that under an online updated auxiliary adaptive control. The auxiliary adaptive control which implements in a sampling-hold fashion can guarantee the convergence of the controlled system. The sufficient conditions that guarantee the states to be steered to a small region near the equilibrium by the proposed MPC are provided. The calculation of the proposed algorithm does not depend on the model mismatch at the starting time. And it does not require the Lyapunov function of the state of the real system always to be reduced at each time. These provide the potential to improve the performance of the closed-loop system. The effectiveness of the proposed method is illustrated through a chemical process example.  相似文献   

19.
何德峰  俞立 《自动化学报》2009,35(12):1558-1563
对状态和输入受约束的Hammerstein系统, 提出一种新的可保证闭环指数稳定的非线性模型预测控制策略. 基于线性子系统镇定的最优控制律, 滚动预测非线性代数方程的解算误差, 继而在线优化计算满足约束的预测控制量. 进一步, 得到闭环系统指数稳定的解算误差上界. 从而闭环系统不仅满足约束而且对解算误差具有鲁棒性. 最后以工业聚丙烯牌号切换控制为例, 仿真验证本文算法的有效性.  相似文献   

20.
1Introduction In recent years the receding horizon control(RHC)ormodel predictive control(MPC)has received muchattention[1].At each sampling time,model predictivecontrol(MPC)yields an optimal control sequence byonline solving a finite horizon open_loop optimizationproblem.Onlythefirst control actionis appliedtothe plantandthe process is repeated at the next samplingtime.The closed_loop stability of model predictive control hasbeenthe focus of research for a long time.In the90’s,through in…  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号