边坡稳定制约下土石坝截面优化遗传算法

为了实现土石坝的截面优化,基于坝体边坡极限平衡状态提出了一种易于施加特性约束条件和进行全局优化搜索的优化遗传算法.建立了以边坡稳定为约束条件的多工况条件下的土石坝截面优化模型,并在Matlab平台上编制了基于极限平衡理论的土石坝坝坡稳定性分析程序以及优化程序,实现了均质土坝在渗流和地震作用等约束下最优截面的搜索.算例结果证明了该优化方法具有一定的有效性和可行性.

基于导重法的叉车门架轻量化设计

针对大型复杂机械设计余量过大的现象,采用导重法和灵敏度计算相结合的集成优化法对某型号内燃叉车的门架结构进行全局轻量化设计.推导强度、刚度等多性态约束条件,搭建导重法单目标、多性态约束优化模型,利用Lamker算法求解Kuhn-Tucker乘子;灵敏度计算采用带有经典误差修正项的半解析法(ESA),引入单元误差修正项,消除刚体转动位移带来的误差;结合导重法,解决了容重和导重难以求解的问题.利用有限元法,对优化后的门架结构进行强度和刚度校核.结果表明,采用导重法和灵敏度计算相结合的集成优化法效果显著,使得叉车门架质量降低18.21%,优化后的门架强度和刚度满足设计要求.

一种桁架结构的拓扑优化设计方法

本文直接从结构力滨三个基本方程出发，构造了一种有效的布局优化设计方法，最优解通过一个线性规划的求解完成，此外，将m+n个性态约束等效为n个独立的性态约束。在线性规划的求解中，需要给定的是应力场而不是位移场。最后，将专家系统应用于结构的机动性分析。几个数值计算结果说明了方法的有效性。

An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization. II. Problems with Path Constraints

This paper considers dealing with path constraints in the framework of the improved control vector iteration （CVI） approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.

Stable MIMO Constrained Predictive Control with Steadystate Objective Optimization

A two-stage multi-objective optimization model-predictive control algorithms(MPC) strategy is presented. A domain MPC controller with input constraints is used to increase freedom for steady-state objective and enhance stabilization of the controller. A steady-state objective optimization algorithm oriented to transient process is adopted to realize optimization of objectives else than dynamic control. It is proved that the stabilization for both dynamic control and steady-state objective optimization can be guaranteed. The theoretical results are demonstrated and discussed using a distillation tower as the model. Theoretical analysis and simulation results show that this control strategy is efficient and provides a good strategic solution to practical process control.

An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization (Ⅰ) Problems Without Path Constraints

This study proposes an efficient indirect approach for general nonlinear dynamic optimization problems without path constraints. The approach incorporates the virtues both from indirect and direct methods: it solves the optimality conditions like the traditional indirect methods do, but uses a discretization technique inspired from direct methods. Compared with other indirect approaches, the proposed approach has two main advantages: (1) the discretized optimization problem only employs unconstrained nonlinear programming (NLP) algorithms such as BFGS (Broyden-Fletcher-Goldfarb-Shanno), rather than constrained NLP algorithms, therefore the computational efficiency is increased; (2) the relationship between the number of the discretized time intervals and the integration error of the four-step Adams predictor-corrector algorithm is established, thus the minimal number of time intervals that under desired integration tolerance can be estimated. The classic batch reactor problem is tested and compared in detail with literature reports, and the results reveal the effectiveness of the proposed approach. Dealing with path constraints requires extra techniques, and will be studied in the second paper.

Global optimization over linear constraint non-convex programming problem

A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.

Inverse problem for Chaplygin’s nonholonomic systems

Chaplygin’s nonholonomic systems are familiar mechanical systems subject to unintegrable linear constraints, which can be reduced into holonomic nonconservative systems in a subspace of the original state space. The inverse problem of the calculus of variations or Lagrangian inverse problem for such systems is analyzed by making use of a reduction of the systems into new ones with time reparametrization symmetry and a genotopic transformation related with a conformal transformation. It is evident that the Lagrangian inverse problem does not have a direct universality. By meaning of a reduction of Chaplygin’s nonholonomic systems into holonomic, regular, analytic, nonconservative, first-order systems, the systems admit a Birkhoffian representation in a star-shaped neighborhood of a regular point of their variables, which is universal due to the Cauchy-Kovalevski theorem and the converse of the Poincaré lemma.

STABILIZATION OF SWITCHED LINEAR SYSTEMS WITH CONSTRAINED INPUTS

The stabilization of switched linear systems with constrained inputs （SLSCI） is considered. The authors design admissible linear state feedbacks and the switching rule which has a minimal dwell time （MDT） to stabilized the system. First, for each subsystem with constrained inputs, a stabilizing linear state feedback and an invariant set of the closed-loop system are simultaneously constructed, such that the input constraints are satisfied if and only if the closed-loop system＇s states lie inside this set. Then, by constructing a quadratic Lyapunov function for each closed-loop subsystem, an MDT is deduced and an MDT-based switching strategy is presented to ensure the stability of the switched system.

A formal structure for symbolic reachability analysis of rectangular hybrid systems

For symbolic reachability analysis of rectangular hybrid systems, the basic issue is finding a formal structure to represent and manipulate its infinite state spaces. Firstly, this structure must be closed to the reachability operation which means that reachable states from states expressed by this structure can be presented by it too. Secondly, the operation of finding reachable states with this structure should take as less computation as possible. To this end, a constraint system called rectangular zone is formalized, which is a conjunction of fixed amount of inequalities that compare fixed types of linear expressions with two variables to rational numbers. It is proved that the rectangular zone is closed to those reachability operations-intersection, elapsing of time and edge transition. Since the number of inequalities and the linear expression of each inequality is fixed in rectangular zones, so to obtain reachable rectangular zones, it just needs to change the rational numbers to which these linear expressions need to compare. To represent rectangular zones and unions of rectangular zones, a data structure called three dimensional constraint matrix(TDCM) and a BDD-like structure rectangular hybrid diagram(RHD) are introduced.