Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles

An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone–Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.     

An integrated approach to hypersonic entry attitude control

This paper presents an integrated approach based on dynamic inversion(DI)and active disturbance rejection control(ADRC)to the entry attitude control of a generic hypersonic vehicle(GHV).DI is frstly used to cancel the nonlinearities of the GHV entry model to construct a basic attitude controller.To enhance the control performance and system robustness to inevitable disturbances,ADRC techniques,including the arranged transient process(ATP),nonlinear feedback(NF),and most importantly the extended state observer(ESO),are integrated with the basic DI controller.As one primary task,the stability and estimation error of the second-order nonlinear ESO are analyzed from a brand new perspective:the nonlinear ESO is treated as a specifc form of forced Li′enard system.Abundant qualitative properties of the Li′enard system are utilized to yield comprehensive theorems on nonlinear ESO solution behaviors,such as the boundedness,convergence,and existence of periodic solutions.Phase portraits of ESO estimation error dynamics are given to validate our analysis.At last,three groups of simulations,including comparative simulations with modeling errors,Monte Carlo runs with parametric uncertainties,and a six degrees-of-freedom reference entry trajectory tracking are executed,which demonstrate the superiority of the proposed integrated controller over the basic DI controller.     

Adaptive tracking control of an autonomous underwater vehicle

This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.     

改进型蚁群算法参数优化研究

研究在不使用局部搜索情况下参数组合对改进型蚁群算法的影响。以带时间窗的车辆路径问题为例,针对基于最大最小蚁群算法的改进蚁群算法中的五个参数,运用均匀设计法对最优参数配置问题进行了研究。仿真实验表明改进的蚁群算法效果明显,能有效解决Solomon数据集中的R类和RC类问题,且具有较强的鲁棒性。对最优参数的局部调整没有明显提高算法获取最优解能力的问题,分析了其可能的原因。     

Sliding‐Mode Velocity Control of a Two‐Wheeled Self‐Balancing Vehicle

Underactuated vehicles are those in which the number of control inputs is less than the degrees of freedom to be controlled. Using actuated wheels, velocity control of the two‐wheeled self‐balancing vehicle drives the vehicle at a desired speed and balances the body of the vehicle. First, we investigate the effects of friction on the wheel and derive the hybrid model of rolling and slipping. Second, we propose a nonlinear sliding mode velocity control scheme for the pure rolling model of the two‐wheeled vehicle. We present the design of the corresponding sliding surfaces and internal dynamics of the two‐wheeled vehicle. Our stability analysis reveals that the proposed sliding mode method can guarantee the asymptotic stability of the error dynamics for velocity control of the underactuated vehicle. Compared to linear optimal control, our numerical simulations demonstrate that the proposed sliding mode schemes can effectively control the velocity under the circumstances of parametric variations, emergency braking, and rapid acceleration in slippery road conditions. The proposed velocity control and the simulation improve our understanding on designing velocity control of the two‐wheeled self‐balancing vehicle.     

基于参数依赖滚动时域H∞控制的高超声速飞行器控制

针对输入受限的高超声速飞行器强耦合、强非线性以及严重不确定性的特点,提出一种参数依赖滚动时域?∞控制(PD-RHHC)的方法.首先在考虑控制输入约束的条件下,引入参数依赖Lyapunov函数和松弛因子并提出了基于LMI优化的PD-RHHC;然后采用函数替换方法,结合张量积模型转换方法实现高超声速飞行器(HSV)纵向非线性弹性模型的LPV描述,并将PD-RHHC应用到高超声速飞行器纵向控制中,以实现HSV在大飞行包线内的机动飞行;最后通过仿真实验验证了所提出算法的有效性.     

求解整合资源条件下的运输调度问题

针对传统的物流运输调度问题(Vehicle Routing Problem,VRP)中车辆之间不协作会造成资源浪费的情况,提出整合资源条件下的运输调度问题(Vehicle Routing Problem with Integration of resources,VRPIR),建立了相应的数学模型。由于混沌具有良好的遍历性,而粒子群优化算法(Particle Swarm Optimization,PSO)具有概念简单,参数少,容易实现等优点,将混沌优化方法引入到粒子群优化算法中,应用混沌粒子群优化算法(Chaos Particle Swarm Algorithm,CPSO)求解VRPIR和VRP,并用CPSO和PSO分别求解VRPIR,实验结果证明该算法优于粒子群优化算法,也证明了提出的VRPIR模型优于VRP,能节省资源,且最小化成本。     

基于混沌蚁群算法的应急救援车辆调度优化

针对应急救援车辆调度优化问题的特征和需求,以可变双向距离、道路风险和成本最小为主要目标,建立了应急救援车辆调度优化问题的多目标优化模型.为避免过早陷入局部最优,提出了基于混沌扰动的改进蚁群系统优化算法.该算法可对信息素进行全局更新混沌扰动,有效地提高了算法的适应性、求解效率和求解质量.仿真实验表明该算法是可行的,能较好地满足应急救援车辆调度的优化需求.     

一种道路车辆监控视频中的关键帧提取方法

针对道路监控视频中特定车辆图像序列的关键帧提取问题,在运动对象检测的基础上,提出一种关键帧提取方法。将积分通道特征和面积特征作为图像特征描述子,结合Ada Boost训练分类器,实现道路监控视频车辆序列图像中关键帧的提取。通过运动对象前景检测技术获得出现在监控区域的运动车辆最小外接矩形图像序列,选择满足监控分析需求(车牌清晰度高,能判断车型)的若干帧作为正样本,其他不满足监控分析需求的作为负样本,提取样本图像的面积特征和积分通道特征,利用Ada Boost方法训练得到一个分类器,使用Ada Boost分类器对测试样本进行分类,根据打分规则提取关键帧。实验结果表明,该方法能提取运动车辆从进入到离开监控区域的序列图像帧中最清晰的图像,实现道路车辆监控视频分析数据的有效压缩。     

汽车灯用热熔胶的研究及性能表征

介绍了生产汽车灯用热熔胶的各种原料及其在胶粘剂中所起的主要作用，为汽车灯用热熔胶原料的选择提供了参考。还介绍了汽车灯用热熔胶的制备过程和厂家所需的各种性能及其表征方法。通过选择适当的原料及配比，生产出性能优良并具有良好市场前景的产品。