基于谐波线性化的滞环电流优化控制

滞环控制的PWM变换器是强非线性环节,难以通过优化控制和常规线性化处理来提高电流控制质量.为此,分析了滞环电流控制原理、谐波线性化方法及系统稳定性,得到了滞环电流控制传递函数模型,时滞和环宽与其他参数的一般关系以及系统自振荡频率的计算方法.根据滞环电流控制的特点,提出了将谐波线性化方法与ITAE优化控制律相结合的方法,以实现对PWM变换器的优化控制.Matlab仿真结果表明了所提出方法的有效性.     

基于滚动优化的对偶控制策略

考虑具有未知参数的随机系统的最优控制问题.提出了一种新的基于滚动优化的对偶控制算法.在动态规划泛函方程中,用Kalman滤波对系统的状态进行估计;用线性化方法对阶段损失函数中的后验概率进行近似,然后,用滚动优化策略对控制与学习之间的耦合关系进行解耦.从而获得了原不可解泛函方程的解析递推表达式和一个易于实施的控制律的解析解.用一个例子说明了控制律的性能,仿真结果表明:该控制律具有良好的对偶性质,在学习和控制之间实现了较好的平衡.     

非线性过程自优化控制的快速算法

针对非线性过程的自优化控制问题,提出了一种求取被控变量的快速算法.不同于线性过程的自优化控制,本文方法基于系统的非线性模型,并且最小化全局平均损失.为快速求解得到的非凸非线性规划问题,作者对其进行了简化.讨论了被控变量解空间的相关特性,阐述了引入的正交酉约束的合理性,并进一步提出了求解次优被控变量的解析法.对一个数值算例和蒸发过程的研究结果表明,提出的快速算法是便捷的、有效的.     

分段线性化多模型动态矩阵控制算法的设计

线性控制算法具有计算简单、实时性好等优点,但是工业过程通常具有复杂的非线性特性,而且非线性程度越强,实际过程和模型之间的误差越来越大,常规的线性控制器性能会随着非线性程度的增强而显著降低.对于一般的非线性控制策略而言,它们具有建模成本较高、难以选取合适的权重因子等难点,本文采用分段线性化的方法,将非线性系统过程分为多个分段的线性化模型,将动态矩阵控制策略推广到各个分段线性模型,设计了分段线性化多模型DMC算法,通过组合以实现其预测控制的目的.为了验证该算法的有效性,本文在Matlab/Simulink软件平台上进行了1套强非线性pH值中和过程实验装置的较全面的控制仿真,包括阶跃及方波给定值跟踪、抗强干扰,以及存在较大模型失配时的给定值跟踪性能仿真.仿真结果表明,针对强非线性的过程,分段线性化的DMC比不分段单模型的DMC过程具有更好的控制性能.     

分段光滑的半监督支持向量分类机

为了解决半监督支持向量分类优化模型中的非凸非光滑问题,基于分段逼近的思想提出了一个分段函数,并以此逼近非凸非光滑的目标函数。给出的分段函数可以根据不同的精度要求选择不同的逼近参数,同时构造出基于上述分段函数的光滑半监督支持向量机模型。采用了LDS(Low Density Separation)算法求解模型,分析了其对对称铰链损失函数的逼进精度。理论分析和数值实验结果都证明分段光滑的半监督支持向量机的分类性能和效率优于以往提出的光滑模型。     

基于风险控制的软件项目过程优化

本文提出一种基于风险控制的软件项目过程评估和优化方法，提出以软件项目风险大小作为评估当前软件项目过程优劣的依据，并从优化软件项目风险控制的视角对软件过程进行优化。给出一个基于风险传递的软件风险优化控制模型和一个动态规划的软件风险控制离散优化算法，以及使用上述方法解决问题的一个示例。本文给出的基于风险控制对软件过程进行事先评估和优化的方法，变以往对软件项目的事后被动控制为事先的积极有效预防，从而可显著提高软件项目的成功率。     

数据驱动的工业过程运行优化控制

现代工业过程机理复杂使得很难对生产过程以及运行指标与被控变量之间关系精确建模.如何基于工业运行过程数据信息,不依赖模型参数给出设定值设计方案,优化运行指标是一挑战性难题.本文针对在稳态附近可以线性化的一类工业过程,考虑运行控制环和底层控制环不同时间尺度,提出一种基于Q--学习方法的次优设定值学习算法.此算法完全利用数据,学习得到次优设定值,实现运行指标以次优的方式跟踪理想值.浮选过程仿真结果表明本文所提方法的有效性.     

基于模糊子集合拟合对非线性系统的模糊控制

实际受控系统都存在非线性和不确定性，使得参数化模型控制算法难以实施．为此，提出一种非参数化模型的模糊控制算法，即在非线性系统中对局部过程实施模糊分段线性化，对分段线性化的过程进行联接，将分段线性化的模糊模型集合拟合为全局非线性模型．其控制律是由局部线性化的模糊控制律经模糊联接而构成整体非线性系统的模糊控制律．通过稳定性证明和仿真验证，论证了所给算法的有效性．     

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SVM和PSO的新型非线性模型预测控制

对于具有强非线性或复杂非线性约束的系统,通过非线性模型的线性化和二次规划优化实现非线性模型预测控制,难以取得满意的结果。提出了一种基于支持向量机模型和粒子群优化的非线性模型预测控制系统的算法。仿真实例表明了支持向量机模型的泛化能力和粒子群优化的寻优速度及能力,证明了将其运用于非线性模型预测控制中的可行性。     

NCO tracking and self-optimizing control in the context of real-time optimization

This paper reviews the role of self-optimizing control (SOC) and necessary conditions of optimality tracking (NCO tracking) as presented by [1]. We show that self-optimizing control is not an alternative to NCO tracking for steady state optimization, but is to be seen as complementary. In self-optimizing control, offline calculations are used to determine controlled variables (CVs), which by use of a lower layer feedback controller indirectly keep the process close to the optimum when a disturbance enters the process. Preferably, the setpoints are kept constant, but they may be adjusted by some optimization layer. Good CVs reduce the need for frequent setpoint changes. When selecting self-optimizing CVs, a set of disturbances must be assumed, as unexpected disturbances are not rejected in SOC. On the other hand, the presented NCO tracking procedure adapts the inputs at given sample times without a model or any assumptions on the set of disturbances. However, disturbances with high frequencies or those which do not lead to a steady state are not rejected. By using NCO tracking in the optimization layer and SOC in the lower control layer, we demonstrate that the methods complement each other, with SOC giving fast optimal correction for expected disturbances, while other disturbances are compensated by the model free NCO tracking procedure on a slower time scale.     

Self-optimizing control of an LNG liquefaction plant

In this paper we apply self-optimizing control (SOC) to a cascaded LNG liquefaction plant. We first introduce the model, and then define the operational objective, which is to achieve minimal energy consumption while satisfying operational constraints. Four control structures are compared; a “standard” temperature control structure, an SOC structure with two plant measurements, an SOC structure that uses a combination of plant measurements as controlled variable, and an SOC structure where we also include measurements of disturbances in addition to the plant measurements. We find that the SOC structures significantly reduce the average steady-state loss when the operating conditions change. We furthermore find that using more plant measurements in the SOC structure results in lowered losses. In particular, for the disturbances considered, the steady-state loss becomes acceptably low, such that there is no need for a supervisory real-time optimization layer. Finally, it has been found that including disturbance measurements results in somewhat reduced losses, although the improvement was insignificant for the studied case. The effectiveness of the SOC framework is shown by closed-loop step responses to selected disturbances.     

一类欠驱动机械系统的非线性控制

针对一类欠驱动机械系统．分析了其数学模型，提出了一种基于部分反馈线性化的非线性控制方案．该方案利用精确线性化的方法将欠驱动系统直接激励部分线性化，将被动部分作为系统的内部动态考虑;并选择直接激励的自由度作为系统输出．进行系统的轨迹跟踪控制;通过分析系统的内部动态，证明了零动态的稳定性能保证控制系统的稳定性．最后通过对吊车的负载防摆控制的研究．验证了该方案的可行性．     

Self-optimizing control – A survey

Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research.     

状态反馈预测控制在双线性系统中的应用

针对可输入输出(I/O)精确线性化的双线性系统,提出了一种直接的预测控制算法。这种算法无需对系统进行线性化,避免了线性化后再引入线性控制方法而带来的复杂运算,并且由于引入状态反馈预测控制的思想,使用实测状态变量反馈,提高了控制系统抑制未知干扰的能力,改善了控制系统的鲁棒性。给出了算法使用条件和推导过程,针对氮合成反应器设计了相应的控制器。仿真结果表明,控制效果良好。     

INPUT‐STATE LINEARIZATION OF A ROTARY INVERTED PENDULUM

The aim of this paper is to design a nonlinear controller for the rotary inverted pendulum system using the input‐state linearization method. The system is linearized, and the conditions necessary for the system to be linearizable are discussed. The range of the equilibriums of the system is also investigated. Further, after the system is linearized, the linear servo controllers are designed based on the pole‐placement scheme to control the output tracking problem. The performance of the controller is studied with different system parameters. The computer simulations demonstrate that the controller can effectively track the reference inputs.     

电力系统非线性切换励磁控制

针对反馈线性化方法难以同时提高电力系统的功角稳定性和电压调节特性的问题,提出了基于分层设计思想的非线性切换励磁控制方法.首先采用逆系统方法实现了系统的反馈线性化,然后根据Lyapunov函数方法对包含零动态的部分线性化系统设计了切换励磁控制器.在电力系统专用仿真平台上的仿真结果证明了所设计的切换控制器能够有效改善功角稳...     

Robust Control System Design for an Uncertain Nonlinear System Using Minimax LQG Design Method

A systematic approach to design a nonlinear controller using minimax linear quadratic Gaussian regulator (LQG) control is proposed for a class of multi‐input multi‐output nonlinear uncertain systems. In this approach, a robust feedback linearization method and a notion of uncertain diffeomorphism are used to obtain an uncertain linearized model for the corresponding uncertain nonlinear system. A robust minimax LQG controller is then proposed for reference command tracking and stabilization of the nonlinear system in the presence of uncertain parameters. The uncertainties are assumed to satisfy a certain integral quadratic constraint condition. In this method, conventional feedback linearization is used to cancel nominal nonlinear terms and the uncertain nonlinear terms are linearized in a robust way. To demonstrate the effectiveness of the proposed approach, a minimax LQG‐based robust controller is designed for a nonlinear uncertain model of an air‐breathing hypersonic flight vehicle (AHFV) with flexibility and input coupling. Here, the problem of constructing a guaranteed cost controller which minimizes a guaranteed cost bound has been considered and the tracking of velocity and altitude is achieved under inertial and aerodynamic uncertainties.     

基于Lie理论的倒立摆系统的控制算法研究

该文通过能量反馈和最优控制相结合的方法实现倒立摆系统的自摆起和稳定控制。在摆起阶段采用能量反馈方法实现快速摆起，而在平衡稳定控制阶段，采用一种非线性系统微分几何方法一李理论，对倒立摆系统进行近似线性化，此种线性化方法使模型更多包含原系统主要的非线性部分，更能逼近实际系统，针对采用李理论得到的近似线性化模型，对倒立摆系统进行最优稳定控制设计。仿真和实时控制试验结果表明，文中提出的李理论近似模型线性化方法对于控制器设计结果是有效的，而且采用的能量反馈和最优控制相结合的联合控制策略能够成功实现倒立摆系统的自摆起和稳定控制过程。