Designing H ∞/GH 2 static-output feedback controller for vehicle suspensions using linear matrix inequalities and genetic algorithms

This paper presents an approach to design the H _∞/GH ₂ static-output feedback controller for vehicle suspensions by using linear matrix inequalities (LMIs) and genetic algorithms (GAs). Three main performance requirements for an advanced vehicle suspension are considered in this paper. Among these requirements, the ride-comfort performance is optimized by minimizing the H _∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road-holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H ₂ (GH ₂) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH ₂ norm as well. A four-degree-of-freedom half-car model with active suspension system is applied in this paper. Several kinds of H _∞/GH ₂ static-output feedback controllers, which use the available sprung mass velocities or the suspension deflections as feedback signals, are obtained by using the GAs to search for the possible control gain matrices and then resolving the LMIs together with the minimization optimization problem. These designed H _∞/GH ₂ static-output feedback controllers are validated by numerical simulations on both the bump and the random road responses which show that the designed H _∞/GH ₂ static-output feedback controllers can achieve similar or even better active suspension performances compared with the state-feedback control case in spite of their simplicities.     

深圳市公交信息查询系统的设计与实现

针对当前公交查询系统存在的缺点，从系统功能、数据库设计、查询算法几个方面对深圳市公交信息查询系统进行研究。该系统采用VISUALBASIC6．0为开发工具，MICROSOFT ACCESS2003为后台数据库，实现了公交站点信息查询、公交线路信息查询，公交线路换乘查询和地图显示等一系列功能。     

Maximizing net benefits for conventional and flexible bus services

Transit ridership is usually sensitive to fares, travel times, waiting times, and access times, among other factors. Therefore, the elasticities of demand with respect to such factors should be considered in modeling bus transit services and must be considered when maximizing net benefits (i.e. “system welfare” = consumer surplus + producer surplus) rather just minimizing costs. In this paper welfare is maximized with elastic demand relations for both conventional (fixed route) and flexible-route services in systems with multiple dissimilar regions and periods. As maximum welfare formulations are usually too complex for exact solutions, they have only been used in a few studies focused on conventional transit services. This limitation is overcome here for both conventional and flexible transit services by using a Real Coded Genetic Algorithm to solve such mixed integer nonlinear welfare maximization problems with constraints on capacities and subsidies. The optimized variables include service type, zone sizes, headways and fares. We also determine the maximum welfare threshold between optimized conventional and flexible services) and explore the effects of subsidies. The proposed planning models should be useful in selecting the service type and optimizing other service characteristics based on local geographic characteristics and financial constraints.     

基于遗传算法的盾构隧道管片接头刚度反演研究北大核心CSCD

梁-弹簧模型在管片衬砌结构计算中应用较广泛,目前接头刚度取值方法主要有模型试验法及经验取值法,模型试验法耗时长、成本高昂,而依据经验取值难免存在一定主观性。鉴于此,利用C++编程语言,基于遗传算法开发了盾构隧道管片结构位移反分析程序,首先通过弹性模量值反演算例,得出反演值与理论值误差为1.4%,验证了算法和程序的可行性。利用程序将反演接头刚度值与实际设计案例管片接头刚度值进行对比,反演计算结果表明:正弯矩下管片环向接头转动刚度值反演误差为2.6%,负弯矩下管片环向接头转动刚度值反演误差为4.3%。     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

An integrated programming model for storage management and vehicle scheduling at container terminals

In this paper, we study the optimization of yard operations, which are critical for the terminal efficiency. A linear mixed integer programming (MIP) model is proposed for scheduling different types of equipment and planning the storage strategy in an integrated way. We also investigate a nonlinear mixed integer programming (NLMIP) model to reduce the number of constraints and the computational time. A set of numerical results are carried out for the comparison between the linear model and the nonlinear model. Finally, we propose a genetic algorithm for the MIP model to illustrate how large scale problems can be solved and to show the effect of different factors on the performances of the optimization model.     

基于燃油经济性的汽车动力传动系匹配优化

传统的汽车传动系匹配研究方法,都是以汽车的动力性或经济性指标为优化目标的单目标优化。为了实现真正意义上的多目标优化,文章利用modeFRONTIER软件和遗传算法的组合优化策略,结合某5挡手动变速车传动系匹配,进行了基于燃油经济性的传动系参数优化设计,达到了降低汽车燃油消耗的效果,为汽车开发设计中传动系匹配优化提供了参考。     

基于遗传算法的汽车拉式离合器膜片弹簧结构参数优化设计

膜片弹簧是膜片弹簧离合器的关键零件。本文通过分析离合器的载荷一变形特性和多种约束条件,在参考成熟离合器结构参数的基础上,提出了一种离合器膜片弹簧的优化设计模型：在摩擦片磨损极限范围内,把弹簧压紧力变化的平均值最小作为优化目标。并采用遗传算法对某轻型货车离合器膜片弹簧进行优化设计计算。结果表明,应用此模型优化后的膜片弹簧各决策变量的值明显优于利用传统经验公式设计所得的结果。无论是在后备系数的稳定性、膜片的最大当量应力方面,还是在分离力的大小方面,均明显优于原设计。     

Updating of Travel Behavior Model Parameters and Estimation of Vehicle Trip Chain Based on Plate Scanning

This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases.     

Lane assignment problem using a genetic algorithm in the Automated Highway Systems

This paper deals with the lane assignment problem in the Automated Highway Systems (AHS). The purpose of lane assignment strategies is to increase throughput on multi-lane highways with many entry and exit points, based on the itineraries of vehicles using the highway (point of entry, time of entry and exit point) and the effects of maneuvers. Using partitioned lane assignment strategies, the lane assignment problem is formulated as an optimization problem to find the proper positions of partitions. Then the optimal problem is solved using a Genetic Algorithm (GA). The proposed algorithm has been validated by comparing its solution with that of an all-cases algorithm for a simple case. Moreover, a traffic simulator with a realistic car-following model as well as lane change logic has been developed. The simulation results show the efficiency of the proposed genetic algorithm, compared with random assignment algorithms.