基于Ptolemy的自适应巡航系统建模与仿真

信息物理融合系统( CPS)是计算、通信与控制技术的融合,汽车自适应巡航控制系统是一种典型的CPS,具有广泛的应用前景。通过建立汽车纵向行驶的数学模型,并基于CPS给出自适应巡航控制系统的结构,设计系统的状态机模型。基于Ptolemy分别设计前车、测量距离和本车的计算模型,构建系统的层次模型,在子模型中采用模态模式对基于时间的模型与状态机模型相结合的混合系统行为进行建模。仿真结果表明,该方法能满足自适应巡航控制系统的要求,保证系统的安全性。     

基于蚁群算法的分布式CPS系统任务调度设计

针对CPS系统中资源和任务的复杂性,同时为了保证CPS系统资源管理和任务调度的快速性、准确性和有效性,构建了分布式CPS系统结构模型,并设计和实现了基于蚁群算法的资源分配策略,在算法中设计了局部和全局信息素更新规则,将资源管理、任务调度和执行相结合,同时兼顾网络的负载平衡问题和服务质量问题.利用Matlab对基于蚁群算法的分布式CPS系统任务调度策略进行了仿真实验,实验结果证明该策略缩短了整体任务调度的总完成时间,并随着任务数的增加,任务时间缩短的效果更加明显.     

网络链路性能参数估计的层析成像方法综述

网络层析成像能够在网络内部节点不提供测量协作的情况下,根据端到端的测量结果,间接地估计网络内部链路性能参数,是一种重要的网络测量手段,能直接指导网络管理和网络优化,目前受到国内外学术界和工业界广泛的关注.在广泛收集国内外资料的基础上,首先总结了目前网络层析成像使用的主要端到端测量方法和技术;再根据不同参数对链路性能刻画程度的不同,将链路性能参数的网络层析成像方法分为两类:定量参数推断方法和定性参数推断方法;然后,针对不同类型参数的估计问题,概括分析了现有算法的特点;最后指出该类方法未来的研究方向与潜在的应用前景.     

Control 5.0: From Newton to Merton in Popper's Cyber-Social-Physical Spaces

The future of control in cyberspace of parallel worlds is discussed. It argues for the coming age of Control 5.0, the control technology for the new IT capable of dealing with artificial worlds with VR, AR, AI and robotics. The discipline of automation needs a new interpretation of its core knowledge and skill set of modeling, analysis, and control for cyber-socialphysical systems, and a paradigm shift from Newtonian Systems with Newton's Laws or Big Laws with Small Data to Mertonian Systems with Merton's Laws or Small Laws with Big Data.      

倒T型导管墙桩基防波堤循环承载力有限元分析

为考虑软土地基循环软化效应,通过二次开发将软土的循环强度嵌入大型通用有限元软件ABAQUS平台中,基于拟静力分析建立倒T型导管墙桩基防波堤结构稳定性计算模型。通过有限元法分析了结构在波浪荷载作用下的循环承载力,并探讨了土体循环软化效应对结构破坏模式产生的影响以及结构尺寸参数对循环承载力的影响。研究结果表明:考虑土体循环软化效应时倒T型导管墙桩基防波堤结构的循环承载力较不考虑土体循环软化效应时极限承载力明显降低,降低程度在28.72%~41.20%之间;考虑土体循环软化效应后,该结构破坏模式有可能发生改变,其破坏模式可能由桩身屈服引起结构失稳转化为土体强度不足导致结构破坏。     

Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems

In this paper, a quasi-Newton-type optimized iterative learning control (ILC) algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.     

三平移并联机器人控制算法

论文对一种三平移并联机器人进行了运动学建模,运用矩阵计算方法得到其机构运动学反解,即根据终端执行器的运动规律,求出原动件在单位时间内的位移,进而求得步进电机在单位时间内所发的脉冲数。实验验证该步进电机控制算法的正确性,为并联机器人的运动控制提供了理论方法。     

重庆羊角滑坡群滑带土抗剪强度研究

羊角滑坡群是乌江白马航电枢纽坝前的一个巨型滑坡群,该滑坡群的稳定与否,对白马航电枢纽的安全运行至关重要.为能给该滑坡群的治理工程设计和稳定分析提供准确的强度参数,对该滑坡群中各滑坡的滑带土抗剪强度进行研究.采用滑带土室内试验、现场大剪试验、反演分析等方法,确定了羊角滑坡群各滑带土的抗剪强度参数,为工程设计提供了可靠的数...     

Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing

The conventional space-time adaptive processing(STAP) method such as the typical sample matrix inversion(SMI)-based STAP method is difficult to implement for a practical system because intense computational complexity arises in calculating the inversion of a space-time covariance matrix directly.According to the block Hermitian matrix property of space-time covariance matrix,a new pulse-order recursive method is proposed in this paper to calculate the inverse covariance matrix for the STAP adaptive weight,which can reduce the computational complexity significantly.The proposed method requires initially calculating the inverse covariance matrix of the first pulse-order recursively based on the block Hermitian matrix property.In the following,the inversion of space-time covariance matrix is obtained recursively based on the previous pulse-order inverse covariance matrix.Next,the STAP adaptive weight is calculated based on the inversion space-time covariance matrix previously obtained.Compared with the conventional SMI-based STAP algorithms,the computational complexity of the proposed method is reduced to more than 50% for the same clutter suppression performance.This method can be applied to practical systems benefiting from small computational complexity and stable clutter suppression performance.