Adaptive dynamic surface control for air-breathing hypersonic vehicle

This paper describes an adaptive control approach for an air-breathing hypersonic vehicle.The control objective is to provide robust altitudes and velocity tracking in the presence of model uncertainties and varying disturbances.A fuzzy-neural disturbance observer is developed to estimate uncertainties and disturbances,and the adaptive controller is synthesized by the dynamic surface approach combing with the observer.The tracking error at the steady state can be guaranteed to converge to inside of a small residue set which the size of the set can be an arbitrary small value.Simulation results demonstrate the effectiveness of the presented approach.     

Robust decentralized H∞ control of multi-channel systems with norm-bounded parametric uncertainties

A robust decentralized H∞ control problem for uncertain multi-channel systems is considered. The uncertainties are assumed to be time-invariant, norm-bounded, and exist in both the system and control input matrices. The dynamic output feedback is mainly dealt with. A necessary and sufficient condition for the uncertain multi-channel system to be stabilized robustly with a specified disturbance attenuation level is derived based on the bounded real lemma, which is reduced to a feasibility problem of a nonlinear matrix inequality (NMI). A two-stage homotopy method is used to solve the NMI iteratively. First, a decentralized controller for the nominal system with no uncertainty is computed by imposing structural constraints on the coefficient matrices of the controller gradually. Then the decentralized controller is modified, again gradually, to cope with the uncertainties. On each stage, a variable is fixed alternately at the iterations to reduce the NMI to a linear matrix inequality (LMI). A given example shows the efficiency of this method.     

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.     

Tool Orientation Optimization and Path Planning for 5-Axis Machining

Tool path generation is a fundamental problem in 5-axis CNC machining, which consists of tool orientation planning and cutter-contact(CC) point planning. The planning strategy highly depends on the type of tool cutters. For ball-end cutters, the tool orientation and CC point location can be planned separately;while for flat end cutters, the two are highly dependent on each other. This paper generates a smooth tool path of workpiece surfaces for flat end mills from two stages: Computing smooth tool orientations on the surface without gouging and collisions and then designing the CC point path. By solving the tool posture optimization problem the authors achieve both the path smoothness and the machining efficiency. Experimental results are provided to show the effectiveness of the method.     

QFT control based on zero phase error compensation for flight simulator

To improve the robustness of high-precision servo systems, quantitative feedback theory (QFT) which aims to achieve a desired robust design over a specified region of plant uncertainty is proposed. The robust design problem can be solved using QFT but it fails to guarantee a high precision tracking. This problem is solved by a robust digital QFT control scheme based on zero phase error (ZPE) feed forward compensation. This scheme consists of two parts: a QFT controller in the closed-loop system and a ZPE feed-forward compensator. Digital QFT controller is designed to overcome the uncertainties in the system. Digital ZPE feed forward controller is used to improve the tracking precision. Simulation and real-time examples for flight simulator servo system indicate that this control scheme can guarantee both high robust performance and high position tracking precision.     

Control of Chaos in Lu System Subject to Mismatch Uncertainties Using Sliding Mode Design

A strategy is proposed to control Lu It is implemented by using sliding mode control system with mismatch uncertainties. The proposed controller ensures the occurrence of the sliding mode motion. It is guaranteed that under the proposed control law, the system state can be regulated to a specific point or in a predictable neighborhood of desired points in the state space even with mismatch uncertainties. Simulation results have illustrated the effectiveness and robustness of the proposed schemes even though the control input is nonlinear.     

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.     

Decentralized adaptive fuzzy control of time-delayed interconnected systems with unknown backlash-like hysteresis

The problem of decentralized adaptive fuzzy control for a class of time-delayed interconnected nonlinear systems with unknown backlash-like hystersis is discussed. On the basis of the principle of variable structure control （VSC） and by using the fuzzy systems with linear adjustable parameters that are used to approximate plant unknown functions, a novel decentralized adaptive fuzzy control strategy with a supervisory controller is developed. A general method, which is modeled the backlash-like hysteresis, is proposed and removes the assumption that the boundedness of disturbance, and the slope of the backlash-like hystersis are known constants. Furthermore, the interconnection term is supposed to be pth-order polynomial in time-delayed states. In addition, the plant dynamic uncertainty and modeling errors are adaptively compensated by adjusting the parameters and gains on-line for each subsystems. By theoretical analysis, it is shown that the closed-loop fuzzy control systems are globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.     

Robust Feedback Controller for Exponential Stability of Nonlinear Systems with Mismatched Uncertainties

The robust stabilization of nonlinear systems with mismatched uncertainties is investigated. Based on the stability of the nominal system, a new approach to synthesizing a class of continuous state feedback controllers for uncertain nonlinear dynamical systems is proposed. By such feedback controllers, the exponential stability of uncertain nonlinear dynamical systems can be guaranteed. The approach can give a clear insight to system analysis. An illustrative example is given to demonstrate the utilization of the approach developed. Simulation results show that the method presented is practical and effective.     

Asia Simulation Conference 2008/the 7th International Conference on System Simulation and Scientific Computing (ICSC’2008) Call for Papers

Asia Simulation Conference 2008/the 7th International Conference on System Simulation and Scientific Computing (ICSC'2008) is a part of the "Asia Simulation Conference", which is an annual     

不确定性结构区间分析的改进Monte Carlo方法

将结构系统中的不确定性参数用区间数来表示，建立了系统的区间有限元控制方程。利用Monte Carlo数值仿真方法对区间有限元控制方程进行求解，得到了结构的位移和应力响应区间。并针对—殷Monte Carlo方法在区间仿真中存在的缺陷，对其进行了改进，使其计算效率大为提高．数值仿真算例表明：经改进的Monte Carlo方法仿真求解计算量大为减少，且能给出较高精度的响应区间，并克服了区间运算中的扩张问题。     

基于改进BPSO的聚类选择性集成

首先针对离散二进制粒子群(binary particle swarm optimization, BPSO)容易陷入局部收敛的问题,提出一种改进的BPSO算法。在分析高斯密度函数对尺度敏感性的基础上,利用粒子群与全局最优粒子的一致性动态调节尺度参数,并利用密度函数对称区间的定积分确定全局最优粒子的变异概率。而后将聚类的选择性集成抽象为组合优化问题,利用聚类成员有效性和差异性的加权组合定义适应度并以改进BPSO的进化过程实现聚类的选择性集成。最后基于标准数据集和图像数据集验证算法的有效性。     

基于蒙特卡洛方法的卫星成本预测模型研究

针对传统的卫星成本预测模型没有包括相应的不确定性因素,提出了基于蒙特卡洛方法的卫星成本预测模型,用以解决主要由技术风险引起的不确定性.针对利用该模型模拟得出的分布曲线可能介于两种标准分布之间的情况,提出了二次拟合检验的思想,提高了拟合的精度.最后,将收集到的相关数据代入该模型并与常用的卫星成本预测方法进行比较,其精度大约提高了10%.可见,使用蒙特卡洛方法进行卫星成本的预测具有较好的精确度.     

植入式PLGA给药系统释药仿真模型研究

针对由MEMS技术制备的多腔体植入式PLGA储库型给药系统的药物释放模型建立问题,基于Monte Carlo方法的溶蚀模型,提出一种PLGA给药系统药物一维释放数学模型.应用MATLAB软件系统编程对给药系统药物释放进行计算机仿真研究,并采用两种药物的实验验证了数学模型的可行性.对影响释药效果的参数分别进行的仿真研究,得出参数对释药效果的影响规律,结果表明可以通过对PLGA降解参数以及释药膜厚度等参数的优化来改善载体的释药特性、延长释药时间.仿真模型的研究结果对于开发新型给药系统具有重要的参考价值.     

基于采样优化的蒙特卡罗移动节点定位算法

针对无线传感器网络中蒙特卡罗移动节点定位算法的不足,提出了一种采样优化的蒙特卡罗移动节点定位算法。该算法根据运动连续性,利用曲线拟合方法,得出样本节点位置后验密度分布取值较大的区域,对该区域内样本节点的权值进行优化,从而完成未知节点的定位。仿真结果表明,改进后的算法能够显著地减少定位所需的样本数,有效提高了无线传感器网络移动节点定位的准确性和鲁棒性。     

基于广义混沌多项式方法的空间站转位不确定性分析

空间站转位是一个长周期过程,需要考虑环境以及自身系统的不确定性因素对整个过程的影响。使用Kane法建立了空间站系统转位的动力学模型,采用广义混沌多项式方法分析了在转位过程中不确定性因素对空间站姿态的影响。仿真结果表明,gPC方法的计算效率和精度优于蒙特卡罗抽样法,并且空间站若从“零状态”开始转位,则不确定性因素会对转位过程造成较大影响。     

基于蒙特卡罗方法的森林冠层BRDF模拟

蒙特卡罗(MonteCarlo)是一种随机统计方法,被广泛用于模拟可见光森林冠层BRDF(双向反射率分布函数)。基于蒙特卡罗方法构建了光子与植被冠层之间的随机过程,给出了蒙特卡罗模拟森林冠层的BRDF的具体实现方法。在此基础上,讨论了蒙特卡罗模拟中的关键技术:光子自由路径的计算;光子散射方向的确定;BRDF热点效应纠正;光子能量收集算法。实践表明,蒙特卡罗方法能准确模拟出森林冠层BRDF,并能作为其他森林冠层BRDF模型的有效检验工具。     

区间概率信息条件下的决策方法

区间概率为描述不确定性提供了一种新工具,但它不满足概率测度的可加性要求.提出了用Monte Carlo模拟法、非线性方法和线性方法将区间概率转化为满足可加性要求的概率测度--传统概率,使得区间概率信息条件下的不确定性决策问题可以通过现有的决策方法进行方案优选.     

基于RPPP的无人机自主着舰关键技术研究

在有风浪的复杂海况下，需要自主着舰的无人机与舰船两者相对运动带有极大不确定性，为了提高无人机着舰时相对定位以及控制的精度，确保无人机着舰时的安全性与可靠性，提出一种通过差分对流层误差的相对精密单点定位技术(relative precise point position, RPPP)。该技术仅依靠数据链和载波型卫星定位接收机，消除相同环境下卫星定位相同误差，获得精确相对定位。将比例导引与LQR(linear quadratic regulator)控制器相结合，解决了无人机着舰入射角偏差较大的问题，提高了无人机着舰末段高程方向及入射角度的控制精度。对无人机着舰轨迹进行规划，建立无人机着舰的运动模型，设计无人机着舰横向和纵向的控制律，搭建无人机自主着舰的仿真平台。仿真结果表明，采用上述算法着舰误差控制在0.2 m以下，入射角偏差在10^-3量级，可满足无人机着舰要求。     

实物期权方法在生产外包中的应用

生产外包对许多企业来说显得越来越重要．因为它能为企业带来管理柔性和竞争优势。生产外包被看成是一种欧式期权并构建实物期权模型进行价值评价。模型中包含产品价格、市场需求、自制生产成本和外包生产成本等四种不确定性源，并通过蒙特卡罗模拟得到生产外包的期权价值。模拟表明，生产外包的期权价值和产品市场价格的变动率、市场需求的变动率及外包成本的变动率是正相关的，而与自制成本的变动率是负相关的。当市场需求的变动率增加、价格的不确定性增加、或者外包成本的不确定性增加，或者自制成本的变动率降低，生产外包就越有利。研究表明实物期权方法对生产外包决策是一种好工具，能够有效地估计外包的期权价值。