A linearization algorithm for optimizing control systems subject to singular value inequalities

The design of feedback linear multivariable systems subject to inequality constraints frequently leads to nonlinear programming problems involving singular values of matrix transfer functions. We present a readily implementable and globally convergent algorithm for solving such problems. It exploits the structure inherent in singular values differently than do other existing methods.     

Minimization of sound radiation in fully coupled structural-acoustic systems using FEM-BEM based topology optimization

This paper presents a method to locally constrain multiple material volume domains for structural optimization with the Level Set Method (LSM). Two different Lagrangian formulations and multiplier update methods are used, for both the global and local problem. The local volume domains can be constrained by both equality and inequality constraints. The optimization objective is compliance minimization for well-posed statically loaded structures. For validation, several example problems are established and solved using the proposed method. Results show that the volume ratios for user established sub-domains can be controlled successfully. The local constraint values are met accurately in the case of equality constraints and remain in their feasible domain in the case of inequality constraints. Optimization results are not significantly hindered by the introduction of local volume constraints for comparable problems.     

Parameterized eigensolution technique for solving constrained least squares problems ∗

This paper aims to introduce an algorithm for solving large scale least squares problems subject to quadratic inequality constraints. The algorithm recasts the least squares problem in terms of a parameterized eigenproblem. A variant of k-step Arnoldi method is determined to be well suited for computing the parameterized eigenpair. A two-point interpolating scheme is developed for updating the parameter. A local convergence theory for this algorithm is presented. It is shown that this algorithm is superlinearly convergent.     

Maximum margin Bayesian network classifiers

We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.     

考虑执行器动态和输入受限的近空间飞行器鲁棒可重构跟踪控制

针对多变量、不稳定的近空间飞行器姿态系统,在系统存在参数不确定和外部干扰的情况下,并考虑执行器动态和输入受限,提出一种鲁棒可重构跟踪控制策略.首先,利用二阶滑模干扰观测器分别重构姿态、角速率回路的复合干扰;其次,采用鲁棒二阶滑模积分滤波器的反推(backstepping)方法避免了控制器设计中微分项膨胀问题,利用鲁棒项抵消重构误差对系统的影响,以实现姿态控制器设计.然后,在考虑执行器动态、输入受限及舵面卡死故障下,给出一种线性矩阵不等式的在线优化舵面分配算法,以实现飞行器的姿态角渐近跟踪期望的制导指令.最后,仿真结果表明所提出的方法具有良好的跟踪控制性能.     

基于正多胞体空间扩展滤波的时变参数系统辨识方法

针对未知但有界噪声时变参数系统,提出了一种基于正多胞体空间扩展滤波的参数辨识方法.采用有界误差方法对测量噪声和参数变化过程进行建模,通过选取最优扩展系数进而扩大正多胞体大小,使得正多胞体包含变化后的参数可行集,由时不变参数系统约束条件构造扩展系数方程,通过线性规划方法求解前k步扩展系数值,选取最大值作为最终扩展系数.采用扩展系数更新每一步时变参数正多胞体约束条件,求解全部参数的上下界得到包裹参数可行域的最紧致正多胞体.仿真示例说明该方法辨识时变参数的有效性和准确性.     

Integrated production and delivery with single machine and multiple vehicles

This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.     

A quasilinearization algorithm and its application to a manipulator problem

A quasilinearization algorithm is proposed for the computation of optimal control of a class of constrained problem. The constraints are inequality constraints on functions of the state and control variables, and bounds on the values of the control variables. Necessary conditions for optimal control of the control problem are derived. In the iterative procedure, no prior information is required regarding the sequence of constrained and unconstrained arcs and the inequality constraints which are on their boundaries along a specific constrained arc of the optimal trajectory. All this information will be determined within the iterative procedure using some necessary conditions for optimal control. The ability of the proposed algorithm to solve practical problems is demonstrated by its application to several variations of two problems, one of which is a common manipulator problem in industry where transportation of open vessels of liquid is to be performed in a specified period of time. It is shown that the proposed quasilinearization algorithm is an effective tool in deriving optimal control policies for a common type of manipulator operation in industry.     

Ultraviolet: A Constraint Satisfaction Algorithm for Interactive Graphics

Ultraviolet is a constraint satisfaction algorithm intended for use in interactive graphical applications. It is capable of solving constraints over arbitrary domains using local propagation, and inequality constraints and simultaneous linear equations over the reals. To support this, Ultraviolet is a hybrid algorithm that allows different subsolvers to be used for different parts of the constraint graph, depending on graph topology and kind of constraints. In addition, Ultraviolet and its subsolvers support plan compilation, producing efficient compiled code that can be evaluated repeatedly to resatisfy a given collection of constraints for different input values.     

An active set algorithm for tracing parametrized optima

Optimization problems often depend on parameters that define constraints or objective functions. It is often necessary to know the effect of a change in a parameter on the optimum solution. An algorithm is presented here for tracking paths of optimal solutions of inequality constrained nonlinear programming problems as a function of a parameter. The proposed algorithm employs homotopy zero-curve tracing techniques to track segments where the set of active constraints is unchanged. The transition between segments is handled by considering all possible sets of active constraints and eliminating nonoptimal ones based on the signs of the Lagrange multipliers and the derivatives of the optimal solutions with respect to the parameter. A spring-mass problem is used to illustrate all possible kinds of transition events, and the algorithm is applied to a well-known ten-bar truss structural optimization problem.     

Topology optimization of structures with manufacturing and unilateral contact constraints by minimizing an adjustable compliance–volume product

In this paper the concept of extended optimality, or hyperoptimality, is adopted. By following this idea, a new compliance–volume product is suggested as objective. The volume appearing in the product is also raised to the power of a new design parameter which can be set to different values. In such manner design concepts with different volume fractions can be generated by using the approach of extended optimality. Both manufacturing constraints and unilateral contact constraints are included in the proposed method. The manufacturing constraints are implemented by adjusting the move limits such that the draw directions are satisfied. Both one draw direction as well as split draw constraints are considered. The contact conditions are modeled by the augmented Lagrangian approach such that the Jacobian in the Newton algorithm as well as in the adjoint equation becomes symmetric. The design parametrization is done by the SIMP model and Sigmund’s filter is utilized when the sensitivities are calculated. The proposed method is very robust and efficient. This is demonstrated by solving problems in both 2D and 3D. The numerical results are also compared to solutions obtained by performing compliance optimization with a constraint on the volume fraction.     

A computational method for parameter optimization problems arising in control†

A parameter optimization procedure is presented for large-scale problems arising in linear control system design that include equality and inequality constraints. The procedure is based on a novel min—max algorithm for locating a constrained relative minimum without the use of penalty functions or slack variables. This algorithm is constructed from an auxiliary minimization problem with equality constraints. Inequality constraints then are introduced using the notion of an effective constraint. Typical problem formulations are discussed, and an extensive design example is presented.     

Output feedback model predictive control for LPV systems based on quasi-min–max algorithm

This paper proposes a robust output feedback model predictive control (MPC) scheme for linear parameter varying (LPV) systems based on a quasi-min–max algorithm. This approach involves an off-line design of a robust state observer for LPV systems using linear matrix inequality (LMI) and an on-line robust output feedback MPC algorithm using the estimated state. The proposed MPC method for LPV systems is applicable for a variety of systems with constraints and guarantees the robust stability of the output feedback systems. A numerical example for an LPV system subject to input constraints is given to demonstrate its effectiveness.     

An intelligent moving object optimization algorithm for design problems with mixed variables, mixed constraints and multiple objectives

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables; a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints; and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder–Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method.     

小数据集情况下基于变权重融合的BN参数学习算法

针对小数据集情况下贝叶斯网络（BN）参数学习结果精度较低的问题，分析了小数据集情况下BN参数变权重设计的必要性，提出一种基于变权重融合的BN参数学习算法VWPL。首先根据专家经验确定不等式约束条件，计算参数学习最小样本数据集阈值，设计了随样本量变化的变权重因子函数；然后根据样本计算出初始参数集，通过Bootstrap方法进行参数扩展得到满足约束条件的候选参数集，将其代入BN变权重参数计算模型即可获取最终的BN参数。实验结果表明，当学习数据量较小时，VWPL算法的学习精度高于MLE算法和QMAP算法的，也优于定权重学习算法的。另外，将VWPL算法成功应用到了轴承故障诊断实验中，为在小数据集上进行BN参数估计提供了一种方法。     

Linear regression with nonstationary variables and constraints on its parameters

The problem of estimating parameters of a linear regression with allowance for inequality constraints on the parameters is considered in the special case when its variables have a trend. A parameter estimation algorithm is described. The consistency of parameter estimates is proved and their asymptotic distribution is found. Consistent estimates are proposed for the mean-square error matrix of estimates of regression parameters and noise dispersion under rather general assumptions on the law of noise distribution. Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 50–64, May–June 2009.     

约束条件下BN参数最大熵模型扩展学习算法

在很多智能系统的参数建模时,用户往往面对建模样本稀少的困境。针对在小数据集条件下贝叶斯网络（BN）参数建模的问题,提出了一种约束数据最大熵BN参数学习算法（CDME）。首先利用小数据集估算BN参数,随后把定性的专家经验转换为不等式约束,并利用Bootstrap算法生成满足约束的一组参数候选集,再根据信息最大熵进行加权计算出BN参数。实验结果表明,当数据量充分时,CDME参数学习算法与经典的MLE算法的学习精度近似,表明了算法的正确性;在小数据集条件下,利用CDME算法,可以对BN进行参数建模,学习精度优于MLE算法和QMAP算法。CDME算法在实际故障诊断样本数据相对稀缺的条件下,获取了诊断BN模型参数,在此基础上完成的诊断推理结果也印证了算法的有效性,为小数据集条件下的参数建模提供了一条新途径。     

具有未知参数的非线性系统动态优化

针对具有未知参数和不等式路径约束的非线性系统动态优化问题,提出一种新颖有效的数值求解方法.首先,将未知参数视为一个动态优化问题的决策变量;其次,利用多重打靶法将无限维的含未知参数动态优化问题转化为有限维的非线性规划问题,进而在不等式路径约束违反的时间段内,用有限多个内点约束替代原不等式路径约束;然后,用内点法求解转化后的非线性规划问题,在路径约束违反的一定容许度下,经过有限多次步数迭代后得到未知参数值的同时得到控制策略,并在理论上对所提出算法的收敛性进行相应证明;最后,对两个经典的含未知参数非线性系统的动态优化问题进行数值仿真以验证所提出算法的有效性.     

A new optimizing parameter approach of LSSVM multiclass classification model

The parameter values of kernel function affect classification results to a certain extent. In the paper, a multiclass classification model based on improved least squares support vector machine (LSSVM) is presented. In the model, the non-sensitive loss function is replaced by quadratic loss function, and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. When the LSSVM is used in multiclass classification, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of classification. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best multiclass classification results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the model.