A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

This paper proposes a new method to solve non convex min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Moreover, the output deviation method is used to design the j-step ahead output predictor. The control law is obtained by the resolution of a non convex min-max optimization problem under input constraints. The key idea is to transform the initial non convex optimization problem to a convex one by means of variable transformations. To this end, the Generalized Geometric Programming (GGP) which is a global deterministic optimization method is used. An efficient implementation of this approach will lead to an algorithm with a low computational burden. Simulation results performed on Multi Input Multi Output (MIMO) system show successful set point tracking, constraints satisfaction and good non-zero disturbance rejection.     

Min-max sliding-mode control for multimodel linear time varying systems

An original linear time-varying system with unmatched disturbances and uncertainties is replaced by a finite set of dynamic models such that each one describes a particular uncertain case including exact realizations of possible dynamic equations as well as external bounded disturbances. Such a tradeoff between an original uncertain linear time varying dynamic system and a corresponding higher order multimodel system with a complete knowledge leads to a linear multi-model system with known bounded disturbances. Each model from a given finite set is characterized by a quadratic performance index. The developed min-max sliding-mode control strategy gives an optimal robust sliding-surface design algorithm, which is reduced to a solution of an equivalent linear quadratic problem that corresponds to the weighted performance indices with weights from a finite dimensional simplex. An illustrative numerical example is presented.     

l1鲁棒辨识:一种递推插值方法

针对l¹鲁棒辨识不能有效利用试验数据和进行在线辨识的问题, 提出了一种在线递推插值辨识方法. 用几何方法描述试验信息, 利用系统可行集与新的试验信息所构成的半空间的包含关系判断数据信息, 有效地利用了试验数据, 提高了辨识精度. 同时提出了一种新的计算辨识误差紧界的方法. 仿真结果表明了算法的有效性和可行性.     

Fuzzy Constrained Min-Max Model Predictive Control Based on Piecewise Lyapunov Functions

This paper proposes two novel stable fuzzy model predictive controllers based on piecewise Lyapunov functions and the min-max optimization of a quasi-worst case infinite horizon objective function. The main idea is to design state feedback control laws that minimize the worst case objective function based on fuzzy model prediction, and thus to obtain the optimal transient control performance, which is of great importance in industrial process control. Moreover, in both of these predictive controllers, piecewise Lyapunov functions have been used in order to reduce the conservatism of those existent predictive controllers based on common Lyapunov functions. It is shown that the asymptotic stability of the resulting closed-loop discrete-time fuzzy predictive control systems can be established by solving a set of linear matrix inequalities. Moreover, the controller designs of the closed-loop control systems with desired decay rate and input constraints are also considered. Simulations on a numerical example and a highly nonlinear benchmark system are presented to demonstrate the performance of the proposed fuzzy predictive controllers.     

Output Integral Sliding Mode for Min-Max Optimization of Multi-Plant Linear Uncertain Systems

We consider the application of a min-max optimal control based on the LQ-index for a set of systems where only the output information is available. Here every system is affected by matched uncertainties, and we propose to use an output integral sliding mode to compensate the matched uncertainties right after the beginning of the process. For the case when the extended system is free of invariant zeros, a hierarchical sliding mode observer is applied. The error of realization of the proposed control algorithm is estimated in terms of the sampling step and actuator time constant. An example illustrates the suggested method of design.      

Cycle time assignment of min-max systems

A variety of problems in computer networks, digital circuits, communication networks, manufacturing plants, etc., can be modelled as discrete event systems with maximum and minimum constraints. Systems with mixed constraints are non-linear and are called min-max systems. The cycle time vector of such a system arises as a performance measure for discrete event systems and provides the appropriate non-linear generalization of the spectral radius. This paper gives a complete account of the cycle time assignment by the state feedback for min-max systems. We describe some new definitions and results about such assignment which generalize the initial earlier works and shed new light on aspects of linear control theory. For an arbitrary min-max system, by introducing the concept of colouring graph and constructing the total condensation and its matrix representation, we give the canonical structure form. In order to design the state feedback system in which the internal structure property is unchanged and the cycle time can be assigned, we introduce and characterize the assignability, uniform state feedback and unmerged assignment for min-max systems. We present an algorithm for the unmerged assignment and illustrate our algorithm by means of an example.     

On discrete dynamic output feedback min-max controllers

The paper considers output feedback min-max controllers for non-square discrete time uncertain linear systems. Based on previous work, it is demonstrated that static output feedback min-max controllers are only realizable for a specific class of systems. To broaden this class, a compensator based framework is proposed to introduce additional degrees of freedom. The conditions for the existence of such dynamic output feedback min-max controllers are given and are shown to be relatively mild. Furthermore, a simple parameterization of the available design freedom is proposed. An explicit procedure is described which shows how a Lyapunov matrix, which satisfies both a discrete Riccati inequality and a structural constraint, can be obtained using Linear matrix inequality optimization. This Lyapunov matrix is used to calculate the robustness bounds associated with the closed-loop system. A simple aircraft example is provided to demonstrate the efficacy of the design approach.     

An approach to multicriterion optimization problems for engineering design

Optimization problems in mechanical engineering design are often modelled as nonlinear programming problems. A multicriterion optimization approach to this problem is developed in this work. The problem formulation is given, and the min-max principle for this problem is discussed. Next, an algorithm is provided for comparing solutions using this principle.The solution which is defined by the min-max principle of optimality may be called the best compromise considering all the criteria simultaneously and on equal terms of importance. This principle is fully formalized mathematically and used to obtain the optimal solution automatically. The algorithm for comparing solutions gives us, from any set of solutions, the one which is optimal in the min-max sense.Seeking the optimal solution in the min-max sense can be carried out in many different ways. Some methods based upon the Monte Carlo method and trade-off studies are proposed.The approach as discussed here is applied to the design of machine tool gearboxes. The problem is formulated as finding the basic constructional parameters (modules, numbers of teeth etc.) of a gearbox which minimizes simultaneously four objective functions: volume of elements, peripheral velocity between gears, width of gearbox and distance between axes of input and output shafts. A detailed example considering a lathe gearbox optimization problem is also presented. This example indicates that for some mechanical engineering optimization problems, using this approach, we can automatically obtain a solution which is optimal and acceptable to the designer.     

计算机网络控制系统实验装置

本文扼要地阐述了自行开发的计算机网络控制系统实验装置。装置控制系统在逻辑结构上分为两层:现场设备层和网络控制层,PROFIBUS用于现场设备层,网络控制层采用工业以太网。文章对网络控制系统的设计思想、系统结构进行详细地分析和论述,并以模糊PID控制实验为例说明系统实验软件的设计方法。     

支持向量机和正交设计方法的比较和应用研究

论文对支持向量机(SVM)和正交设计方法进行了比较。支持向量机在分类和回归方面广为应用，而正交设计方法在实验设计方面是非常有效的，并且在化学工业中应用广泛。本文使用了两因素、七维正交实验(干燥实验)作为例子来把支持向量机方法应用到实验设计中去。正交表是用来研究实验的最优化条件和显著因素，本文给出了支持向量机和正交实验设计的计算结果。通过两者的比较，可以看到支持向量在实验预测方面比正交设计方法效果更好。从而可知支持向量机在实验设计方面的前景广阔。     

电网大数据量表设计优化技术及应用

阐述了在数据库设计阶段实施大数据量表设计优化工作的必要性.介绍了电网大数据量表的概念及产生原因,并针对因大数据量表而引发的数据库性能问题,提出了大数据量表设计优化相关的关键技术.在设备状态检修系统的数据库设计阶段,以设备状态评价功能为例进行实践应用.仿真实验表明对大数据量表实施设计优化后系统性能得到了显著提升.为后续系统稳定运行提供保障,也为类似的大数据量表设计优化应用提供了参考.     

一种按钮可靠性测试装置的设计

以电梯按钮的可靠性测试试验为背景,设计了一套由工控机、PLC、调速电动机、步进电动机、传感器等设备组成的测试装置,主要介绍了测试装置控制系统设计、软件设计、可靠性试验步骤和方法。该装置已成功应用于可靠性测试试验中,运行稳定、可靠。     

Unprejudiced optimal open loop input design for identification of transfer functions

The problem to estimate transfer functions of linear systems is considered. The quality of the resulting estimate depends, among other things, on the input used during the identification experiment. We measure the quality using a quadratic norm in the frequency domain. The problem to determine optimal inputs, i.e. inputs that minimize the chosen norm, subject to constrained input variance, has long been studied. We point out that such procedures may involve a prejudice (that the system is to be found in a certain model set) that may have some surprising effects. We discuss how such a prejudice can be reduced by allowing the possibility that the true system cannot be exactly described in the chosen model set. We also calculate explicit expressions for the resulting “unprejudiced” optimal inputs. These expressions relate the signal-to-noise ratio (as a function of frequency) to the chosen weighting function in the quadratic norm. We also point out the role of the employed noise model for the design.     

基于CGI嵌入式监控系统动态数据交互的实现

在简要介绍了CGI技术及其特点之后，结合嵌入式远程监控系统的基本需求，提出了组件化设计的思想，并定义了嵌入式远程监控系统的主要组件。结合CGI组件的设计和实现，对基于CGI技术实现动态数据交互的方法进行了探讨，并给出部分程序实例。     

The neutral case for the min-max angle criterion: a generalized concept

Recently, Hansford (1990) has investigated an interesting construction of a neutral set for the min-max angle criterion. The technique described by her requires three data points to obtain the locus of the fourth point. We have observed that the min-max neutral set may be obtained even with two data points and a given angle. It has been further noticed that the min-max and the max-min neutral sets may be easily compared in some special cases.     

Virtual reference feedback tuning: a direct method for the design of feedback controllers

This paper considers the problem of designing a controller for an unknown plant based on input/output measurements. The new design method we propose is direct (no model identification of the plant is needed) and can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the case of restricted complexity controller design, the achieved controller is a good approximation of the restricted complexity global optimal controller. A simulation example shows the effectiveness of the method.     

UML在IFIS智能指纹鉴别系统开发中的应用

ML是一种面向对象的建模语言,广泛应用于以用例为驱动、以体系结构为中心、迭代及增量的软件开发过程中。文中主要阐述了UML面向对象分析和设计思想在IFIS智能指纹鉴别系统开发过程中的应用,包括需求分析、系统设计和实现。     

一种支持分布式进程迁移的动态负载平衡征募算法的研究

负载平衡是分布式系统必须考虑的问题,本文介绍的征募算法独立于网络拓扑结构,其思想可以应用到分布式系统中,征募算法的设计思想向传统负载平衡算法提出了挑战,它不但克服了投标算法的缺点,而且在减小通讯开销和提高处理机利用率两方面作了很多努力,使其成为一种高效的分布式进程迁移和动态负载平衡策略。我们在分布式ＵＮＩＸ系统上实现并验证了征募算法的高效性。     

Model Predictive Control oriented experiment design for system identification: A graph theoretical approach

We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.     

Identification for control: Optimal input intended to identify a minimum variance controller

It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure.