Topology optimization of structure for dynamic properties considering hybrid uncertain parameters

In the design and manufacturing of mechanical components, the dynamic properties of continuum structure are one of the most significant performances. At the same time, the uncertainty is widespread in these dynamic problems. This paper presents a robust topology optimization methodology of structure for dynamic properties with consideration of hybrid uncertain parameters. The imprecise probability uncertainties including materials, geometry and boundary condition are treated as an interval random model, in which the probability distribution parameters of random variables are modeled as the interval variables instead of given precise values. Two dynamic properties, including dynamic-compliance and eigenvalue, are chosen as the objective function. In addition, different excitation frequency or eigenvalue is discussed. In this work, the bi-directional evolutionary structural optimization (BESO) method is adopted to find the optimal robust layout of the structure. A series of numerical examples is presented to illustrate the optimization procedure, and the effectiveness of the proposed method is demonstrated clearly.     

MPEC strategies for optimization of a class of hybrid dynamic systems

With the development and widespread use of large-scale nonlinear programming (NLP) tools for process optimization, there has been an associated application of NLP formulations with complementarity constraints in order to represent discrete decisions. In particular, these constraints arise frequently in equation-based formulations for real-time optimization. Also known as mathematical programs with equilibrium constraints (MPECs), these formulations can be used to model certain classes of discrete events and can be more efficient than a mixed integer formulation, particularly for large systems with many discrete decisions, such as dynamic systems with switches at any point in time. In this study, we consider and extend MPEC formulations for the optimization of a class of hybrid dynamic models, where the differential states remain continuous over time. These include differential inclusions of the Filippov type. Here, particular care is required in the formulation in order to preserve smoothness properties of the dynamic system. Results on three case studies, including process control examples, illustrate the effectiveness and accuracy of the proposed MPEC optimization methodology for a class of hybrid dynamic systems.     

Data-driven,using design of dynamic experiments,versus model-driven optimization of batch crystallization processes

A new data-driven experimental design methodology, design of dynamic experiments (DoDE), is proposed as a means of developing a response surface model that can be used to effectively optimize batch crystallization processes. This data-driven approach is especially useful for complex processes for which it is difficult or impossible to develop a knowledge-driven model in a timely fashion for the optimization of an industrial process. Design of dynamic experiments [1] generalizes the formulation of time-invariant design variables from design of experiments, allowing for consideration of time-variant design variables in the experimental design. When combined with response surface modeling and an appropriate optimization algorithm, a data-driven optimization methodology is produced, which we call DoDE optimization. The method is used here to determine the optimal cooling rate profile, which integrates to give the optimum temperature profile, for a batch crystallization process. To examine the effectiveness of the DoDE optimization method, the data-driven optimum temperature profile is compared to the optimum temperature profile obtained using a model-based optimization technique for the potassium nitrate–water batch crystallization model developed by Miller and Rawlings [2]. The temperature profiles calculated using DoDE optimization yield response values within a few percent of the true model-based optimum values. A sensitivity analysis is performed on one case study to evaluate the distribution of the response variable from each method in the presence of parameter and initial seed distribution variability. It is demonstrated that there is partial overlap in the distributions when only variability in the model parameters is evaluated and there is substantial overlap when variability is included in both the model and initial seed distribution parameters. From this evidence, it can be concluded that the DoDE optimization method has the potential to be a useful data-driven optimization tool for batch crystallization processes where a first-principles model is not available or cannot be developed due to time and/or cost constraints.     

变风量空调系统的迭代学习控制研究

针对变风量（VAV）空调系统下位机设定点变动时,整个系统完全达到稳态时间过长,且各子系统易出现超调的问题,提出采用一种迭代学习控制（ILC）的设定值序列优化方法。以空调系统中变频风机—管道静压控制回路为实例,说明该方法的可行性。采用递推最小二乘法（RLS）建立该回路的动态模型,并给出了一种新的迭代学习期望轨迹,应用迭代学习PD控制律对其动态过程进行仿真分析,并将此算法用于空调实验平台验证其控制效果。结果表明,ILC可以改善空调子系统的动态特性,为VAV空调系统的全局稳态优化奠定了基础。     

无人值守放牧监测系统报警准确率的研究

通过ZigBee技术、GPS定位追踪技术和远程无线通信技术的结合,计算了ZigBee节点传输距离,并提出一种双系统的监测牛群数量的策略,通过对系统误报率的分析,设定误报率门限,从而确定双系统的组成方式,提高牛群数量监测的准确性,降低误警概率.在此基础上设计了一套智能牛群检测系统,并通过实验验证了理论计算结果.该系统可以在无人值守的情况下,自动监测草原牧场上牛群数量并记录牛群位置,实现自动上传功能.     

Identification of multibody vehicle models for crash analysis using an optimization methodology

This work proposes an optimization methodology for the identification of realistic multibody vehicle models, based on the plastic hinge approach, for crash analysis. The identification of the design variables and the objective function and constraints are of extreme importance for the success of the optimization. The characteristics of the plastic hinges are used as design variables while the objective functions are formulated with measures of the difference between the dynamic response of the model and a reference response. The sequential application of genetic and gradient-based optimization methods is used to solve the optimization problem constituting a systematic approach to the automatic identification of vehicle multibody models. The methodology is demonstrated with the identification of the multibody model of a large family car for side and front crash. The vehicle model is developed in the MADYMO multibody code which is linked with the optimization algorithms implemented in the Matlab Optimization Toolbox.     

Application of Kalman filtering to demographic models

In this paper demographic systems appear as stochastic and unstable linear systems. Their state representation model is simulated and identified using Kalman filtering. The example of the French beef cattle herd is presented and results are discussed with emphasis on their agroeconomic meaning.     

Optimization of flexible components of multibody systems via equivalent static loads

An optimization methodology that iteratively links the results of multibody dynamics and structural analysis software to an optimization method is presented to design flexible multibody systems under dynamic loading conditions. In particular, rigid multibody dynamic analysis is utilized to calculate dynamic loads of a multibody system and a structural optimization algorithm using equivalent static loads transformed from the dynamic loads are used to design the flexible components in the multibody dynamic system. The equivalent static loads, which are derived from equations of motion, are used as multiple loading conditions of linear structural optimization. A simple example is solved to verify the proposed methodology and the pelvis part of the biped humanoid, a complex multibody system which consists of many bodies and joints, is redesigned using the proposed methodology.     

Pressure regulation inside a hypersonic wind tunnel using H-infinity optimization control

Hypersonic wind tunnel is a ground-based facility used to study the aerodynamic properties of space vehicles during re-entry. This paper aims at designing an H-infinity controller with krill herd optimization algorithm to regulate pressure inside the settling chamber of a hypersonic wind tunnel. The krill herd algorithm is a novel stochastic algorithm for improving the performance characteristics by optimizing the H-infinity controller parameters. The proposed algorithm minimizes the H-infinity norms by tuning the controller weighing function parameters. The dynamic characteristics of the settling chamber pressure with H-infinity and H-infinity control based on krill herd algorithm is studied by numerical simulations. The proposed algorithm is highly efficient and robust in controlling the settling chamber pressure in terms of performance parameters.     

车队速度滚动时域动态规划及非线性控制

考虑自主车辆队列的节能安全问题，本文提出一种车辆队列协同控制方法，该方法可保证车队低能耗安全行驶.首先，充分考虑道路坡度以及车队异质性建立车队非线性模型，利用基于油耗模型的优化指标构建车队速度优化问题，提出一种滚动时域动态规划算法（Receding horizon dynamic programming，RHDP），获得车队的参考速度.然后，基于非线性车辆模型，运用反步法设计车辆跟踪控制器，并进行车队队列稳定性分析.这种协同控制方法的有效性已通过数值仿真和智能交通实验平台的验证.     

Robust optimization of structural dynamic characteristics based on adaptive Kriging model and CNSGA

Dynamic characteristics greatly influence the comprehensive performance of a structure. But they are rarely included as objectives in traditional robust optimization of structures. In this study, a robust optimization model including both means and standard deviations of dynamic characteristic indices in the objective and constraint functions is constructed for improving the structural dynamic characteristics and reducing their fluctuations under uncertainty. Adaptive Kriging models are employed for the efficient computation of dynamic characteristics. An intelligent resampling technology is proposed to save computational costs and accelerate convergence of Kriging models, which takes full advantage of the test points for precision verification, the sample points within the local region of the biggest relative maximum absolute error and the near-optimal point to improve the global and local precision of Krigings. The high efficiency of proposed intelligent resampling technology is demonstrated by a numerical example. Finally, an efficient algorithm integrating adaptive Kriging models, Monte Carlo (MC) method, constrained non-dominated sorting genetic algorithm (CNSGA) is proposed to solve the robust optimization model of structural dynamic characteristics. Kriging models are interfaced with MC method to efficiently compute the fitness of individuals during CNSGA. The implementation of proposed methodology is explained in detail and highlighted by the robust optimization of a cone ring fixture with lots of circumferentially distributed holes in a large turbo generator aimed at moving its natural frequencies away from the exciting one. The comparison of the optimized design with the initial one demonstrates that the proposed methodology is feasible and applicable in engineering practice.     

溢油应急决策中的物资调度方法

针对多出救点多物资多目标应急调度问题的特点,并结合溢油敏感区等对响应时间的限制,提出了基于不同响应时间段的动态优化模型．以应急救援时间最早和出救点数目最少为优化目标,对不同响应时间段分别运用理想点法和构造剔除集合确定最优调度方案．最后通过算例验证了所提出调度方法的可行性．     

Multistage portfolio optimization with stocks and options

We develop a multistage portfolio optimization model that utilizes options for mitigating market risk in a dynamic setting. Due to the key role of scenarios in the quality of investment decisions, a new scenario generation method is proposed that characterizes the dynamic behavior of asset returns. This methodology takes the dependence structure of different asset returns into account, and also considers serial correlations of each of the asset returns. Moreover, it preserves marginal distributions of asset returns. Also, it precludes arbitrage opportunities. To investigate the role of options, we implement the scenario generation method on a set of stocks selected from the New York Stock Exchange. Results show the high performance of the proposed scenario generation method. Afterwards, the generated set of scenarios is used as the uncertainty set for the multistage portfolio optimization model. Static and dynamic assessments are used for measuring the performance of options in mitigating market risks and generating additional returns. Finally, backtesting simulations are used for assessing different trading strategies of options.     

自适应磷虾群优化Elman神经网络的目标威胁评估

提出一种自适应磷虾群算法,在基本磷虾群算法中引入遗传繁殖机制,并加入进化算子和优化算子构成自适应环节,提高了算法的全局搜索能力和预测精度;通过自适应磷虾群算法对Elman神经网络的初始权值和阈值进行寻优,并在此基础上建立目标威胁评估模型。仿真实验表明,自适应磷虾群优化Elman神经网络既保证了一定的收敛速度,又能够使寻优精度得到明显提升,其对测试集的预测结果优于传统Elman神经网络和基本磷虾群优化Elman神经网络,从而验证了算法模型在目标威胁评估中的可行性、有效性。     

基于间歇反馈多维泰勒网的动力学模型

具有羊群效应的系统,因为结构复杂,内部要素互相影响,参与其中的人类行为难以预测而不易建立整体模型. 本文提出建立间歇反馈多维泰勒网动力学模型方法,通过叠加的死区函数模拟了羊群效应中从众心理对系统产生的间歇反馈,结合多维泰勒网建立了适合普遍系统的一般动力学方程. 该方法区别于以往羊群效应的以个体研究为出发点,通过系统对外表征的数据波动建立了含有羊群效应的整体模型. 实例结果表明,这种基于间歇反馈多维泰勒网的动力学建模方法是可行和有效的.     

Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization

This paper presents a new approach for short-term hydropower scheduling of reservoirs using an immune algorithm-based particle swarm optimization (IA-PSO). IA-PSO is employed by coupling the immune information processing mechanism with the particle swarm optimization algorithm in order to achieve a better global solution with less computational effort. With the IA-PSO technique, the hydro-electrical optimization model of reservoirs is formulated as a high-dimensional, dynamic, nonlinear and stochastic global optimization problem of a multi-reservoir hydropower system. The purpose of the proposed methodology is to maximize total hydropower production. Here it is applied to a reservoir system on the Qingjiang River, in the Yangtze watershed, that consists of two reservoirs. The results are compared with the results obtained through conventional operation method, the dynamic programming and the standard PSO algorithm. From the comparative results, it is found that the IA-PSO approach provides the most globally optimum solution at a faster convergence speed.     

An efficient reliability-based optimization scheme for uncertain linear systems subject to general Gaussian excitation

A very efficient methodology to carry out reliability-based optimization of linear systems with random structural parameters and random excitation is presented. The reliability-based optimization problem is formulated as the minimization of an objective function for a specified reliability. The probability that design conditions are satisfied within a given time interval is used as a measure of the system reliability. Approximation concepts are used to construct high quality approximations of dynamic responses in terms of the design variables and uncertain structural parameters during the design process. The approximations are combined with an efficient simulation technique to generate explicit approximations of the reliability measures with respect to the design variables. In particular, an efficient importance sampling technique is used to estimate the failure probabilities. The number of dynamic analyses as well as reliability estimations required during the optimization process are reduced dramatically. Several example problems are presented to illustrate the effectiveness and feasibility of the suggested approach.     

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of \(\hbox {Prometheus}^{\mathrm{TM}}\) methodology and programmed in \(\hbox {JACK}^{\mathrm{TM}}\) agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms.     

Simultaneous design and control of processes under uncertainty: A robust modelling approach

This paper presents a new methodology to integrate process design and control. The key idea in this method is to represent the system’s closed-loop nonlinear behaviour as a linear state space model complemented with uncertain model parameters. Then, robust control tools are applied to calculate bounds on the process stability, the process feasibility and the worst-case scenario. The new methodology was applied to the simultaneous design and control of a mixing tank process. The resulting design avoids the solution of computationally intensive dynamic optimizations since the integration of design and control problem is reduced to a nonlinear constrained optimization problem.     

Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.