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1.
基于鲁棒控制Lyapunov 函数的非线性预测控制   总被引:1,自引:1,他引:0  
针对一类约束不确定性非线性仿射系统,提出一种可保证闭环系统鲁棒镇定的非线性模型预测控制算法.利用鲁棒控制Lyapunov函数得到改进的Sontag公式,并以此为基础,构造一种计算有效的单自由度鲁棒预测控制器.以Matlab语言为仿真工具,对一开环不稳定振荡器进行了仿真研究,结果表明,利用该控制算法得到的闭环系统不仅渐近稳定于原点,而且所得控制量和系统状态都满足系统约束,从而验证了控制算法的有效性.  相似文献   

2.
具有Hammerstein形式的非线性系统广义预测控制   总被引:14,自引:2,他引:12  
本文提出了具有Hammerstein形式的非线性系统广义预测控制方法,分析了当控制水平等于1时闭环系统的稳定性,同时还提出了使用线性估计器的非线性自适应广义预测控制算法。仿真结果表明了算法的有效性。  相似文献   

3.
本文以SISO非线性系统的输入-输出关系为对象研究扩展线性化方法。不仅讨论了I/O扩展线性化状态反馈的存在性。而且给出了该状态反馈的详细设计步骤。最后,对一个实例进行了仿真计算。  相似文献   

4.
动态矩阵控制算法是一种基于对象阶跃响应的预测控制算法,适用于控制系统复杂、数学模型难以精确建立的过程。针对输入/输出无约束的模型控制器设计,采用动态矩阵控制方法,包括预测模型、滚动优化、误差校正和闭环控制形式。通过MATLAB对它的仿真,验证了闭环系统鲁棒性较好,系统性能容易满足要求。结果表明,动态矩阵预测控制算法控制明显,因此是一种最优控制技术。  相似文献   

5.
侯明冬  王印松 《控制与决策》2018,33(9):1591-1597
针对一类包含扰动的非线性离散时间系统,提出一种新的无模型自适应离散积分终端滑模控制算法.该算法基于紧格式动态线性化数据模型,利用离散积分终端滑模控制算法设计无模型自适应控制器,并采用扰动估计技术估计系统的扰动项,其中动态线性化方法中“伪偏导数”的估计算法仅依赖于被控系统的I/O测量数据.理论分析证明了系统输入输出有界,并通过仿真实验验证了所提算法的有效性.  相似文献   

6.
马乐乐  刘向杰 《自动化学报》2019,45(10):1933-1945
迭代学习模型预测控制是针对间歇过程的先进控制方法.它能通过迭代高精度跟踪给定参考轨迹,并保证时域上的闭环稳定性.然而,现有的迭代学习模型预测控制算法大多基于线性/线性化系统,且没有考虑参考轨迹变化的情况.本文基于线性参变系统提出一种能有效跟踪变参考轨迹的鲁棒迭代学习模型预测控制算法.首先,采用线性参变模型准确涵盖原始非线性系统的动态特性.然后,将鲁棒H控制与传统迭代学习模型预测控制相结合,抑制变参考轨迹带来的跟踪误差波动,通过优化线性矩阵不等式约束下的目标函数求得控制输入.深入分析了鲁棒迭代学习模型预测控制的鲁棒稳定性和迭代收敛性.最后,通过对数值例子和连续搅拌反应釜系统的仿真验证了所提出算法的有效性.  相似文献   

7.
文中介绍了三种实用的PLC I/O点扩展方法,运用这些扩展方法可以提高I/O点利用率,实现小容量PLC控制较大系统.并将这些方法成功地应用于舞台机械控制系统中.  相似文献   

8.
针对一类具有状态约束和控制约束的离散非线性系统,本文采用有限维参数化方法提出了一种基于闭环优化的H∞鲁棒预测控制算法.这种算法把预测控制的滚动优化机制同微分对策理论和非线性H∞控制理论做了有机结合;在闭环优化中通过有限维参数化方法把控制变量参数化为多项式控制变量,并且引入被控系统的过渡平衡点.这样算法不但可以处理不确定系统,而且降低了在线闭环优化的计算复杂度.进一步,证明了算法的可行性和对有界不确定性系统的鲁棒稳定性.最后,用数值仿真验证了算法的有效性.  相似文献   

9.
本地I/O扩展还是远程I/O扩展?这个问题完全可以由用户自己来决定,X20的总线控制器模块能够让客户自由、灵活地选择各种系统架构,让我们的用户真正做到:应用决定架构。  相似文献   

10.
基于反步设计的构造性非线性预测控制算法   总被引:1,自引:0,他引:1  
针对具有状态和输入约束的严格反馈非线性系统,提出一种反步设计构造性非线性预测控制算法.利用反步设计法离线构造系统的控制李亚普诺夫函数,进而得到系统的镇定可调控制器即稳定控制类.基于性能指标,滚动优化控制器可调参数,计算满足系统约束的预测控制量.进一步,运用控制李亚普诺大函数的性质建立闭环系统的稳定性.最后,应用轮式移动机器人的优化控制验证本文结果的有效性.  相似文献   

11.
都明宇  刘桂芝 《计算机仿真》2007,24(3):173-175,291
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.  相似文献   

12.
针对高炉炼铁过程,本文提出一种基于即时学习的高炉铁水质量自适应预测控制方法(JITL–APC).该方法的特点是控制器通过k向量近邻(k–VNN)方法搜索数据库中的输入输出(I/O)数据信息,对非线性系统进行局部建模,并在此基础上计算控制律.而且,该方法中引入了工业异常数据处理机制,利用JITL学习子集中的平均数据项,对异常数据项进行填补或替换,从而消除异常数据对控制系统的影响.此外,本文提出一种JITL模型保留策略(MRS),避免由于数据库中相似数据样本不足导致的局部模型严重失配,并通过实时收集I/O数据更新数据库,使控制器自适应不同的工况条件,MRS还可以有效抑制噪声干扰的影响,从而提高控制系统的稳定性.最后,基于某大型钢铁厂2#高炉的数值仿真实验,充分验证了该方法的有效性.  相似文献   

13.
In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.  相似文献   

14.
针对车辆横摆稳定性控制问题,本文提出一种基于扩张状态观测器的线性模型预测控制器设计方法.首先,将非线性车辆模型线性化,建立带有模型误差干扰项的线性模型,其中线性化导致的模型误差采用扩张状态观测器估计得到,并证明了观测器的稳定性.然后基于此模型设计线性预测控制器,近似实现了非线性预测控制器的控制效果,同时降低了计算量.最后,通过不同路况下的仿真实验结果,验证了所提方法的计算性能和控制效果.  相似文献   

15.
This work proposes a new adaptive terminal iterative learning control approach based on the extended concept of high‐order internal model, or E‐HOIM‐ATILC, for a nonlinear non‐affine discrete‐time system. The objective is to make the system state or output at the endpoint of each operation track a desired target value. The target value varies from one iteration to another. Before proceeding to the data‐driven design of the proposed approach, an iterative dynamical linearization is performed for the unknown nonlinear systems by using the gradient of the nonlinear system with regard to the control input as the iteration‐and‐time‐varying parameter vector of the equivalent linear I/O data model. By virtue of the basic idea of the internal model, the inverse of the parameter vector is approximated by a high‐order internal model. The proposed E‐HOIM‐ATILC does not use measurements of any intermediate points except for the control input and terminal output at the endpoint. Moreover, it is data‐driven and needs merely the terminal I/O measurements. By incorporating additional control knowledge from the known portion of the high order internal model into the learning control law, the control performance of the proposed E‐HOIM‐ATILC is improved. The convergence is shown by rigorous mathematical proof. Simulations through both a batch reactor and a coupled tank system demonstrate the effectiveness of the proposed method.  相似文献   

16.
Recurrent neuro-fuzzy networks for nonlinear process modeling   总被引:14,自引:0,他引:14  
A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input/output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process I/O data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learnt. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.  相似文献   

17.
In this paper, the H input/output (I/O) linearization formulation is applied to design an inner‐loop nonlinear controller for a nonlinear ship course‐keeping control problem. Due to the ship motion dynamics are non‐minimum phase, it is impossible to use the ordinary feedback I/O linearization to resolve. Hence, the technique of H I/O linearization is proposed to obtain a nonlinear H controller such that the compensated nonlinear system approximates the linear reference model in I/O behaviour. Then a μ‐synthesis method is employed to design an outer‐loop robust controller to address tracking, regulation, and robustness issues. The time responses of the tracking signals for the closed‐loop system reveal that the overall robust nonlinear controller is able to provide robust stability and robust performance for the plant uncertainties and state measurement errors. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

18.
根据静止无功发生器(SVG)数学模型的非线性特性,提出了微分几何变结构控制方法,运用微分几何精确线性化理论,把非线性系统转化成了一个线性系统,在此基础上应用非线性变结构控制理论进行设计控制器。结果表明,微分几何变结构控制方法对补偿SVG的无功电流具有有效性和可行性。  相似文献   

19.
This paper describes the application of nonlinear model predictive control (NMPC) to the temperature control of a semi-batch chemical reactor equipped with a multi-fluid heating/cooling system. The strategy of the nonlinear control system is based on a constrained optimisation problem, which is solved repeatedly on-line by a step-wise integration of a nonlinear dynamic model and optimisation strategy. A supervisory control routine has been developed, based on the same nonlinear dynamic model, to handle automatically the fluid changeovers. Both NMPC and supervisory control have been implemented on a PC and applied to a 16 l batch reactor pilot plant. Experiments illustrate the feasibility of such a procedure involving predictive control and supervisory control.  相似文献   

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