基于自然元法和精细积分法的功能梯度材料瞬态热传导问题求解

为促进无网格法分析技术在热传导分析中的应用,提出空间离散采用自然单元法、时间离散采用精细积分法求解功能梯度材料瞬态热传导问题的数值计算方法.在计算过程中,取高斯点的材料参数模拟功能梯度材料特性的变化.温度场采用自然邻接点插值形函数进行离散插值.数值算例验证该数值算法的正确性和有效性.     

Dynamic disturbance rejection controllers for neutral time delay systems with application to a central heating system

In the present paper the problem of disturbance rejection of single input-single output neutral time delay systems with multiple measurable disturbances is solved via dynamic controllers. In particular, the general form of the controller matrices is presented, while the necessary and sufficient conditions for the controller to be realizable are offered. The proposed technique is applied to a test case neutral time delay central heating system. In particular, the nonlinear model of the plant and its linearized approximation are presented. Based on the linearized model, a two-stage controller is designed in order to regulate the room temperature and the boiler effluent temperature. The performance of the closed loop system is investigated through computational experiments.     

基于支持向量机的参数自整定PID非线性系统控制

对非线性系统提出了一种基于支持向量机的自整定PID控制新方法.用支持向量机辨识系统的非线性关系,并对之进行线性化,提取出瞬时线性模型,采用最小方差的准则获取PID控制器的最优参数.为改善控制器的性能,提出了一些改进措施,包括使用一阶滤波器、控制器参数更新标准及惩罚系数的调整等.通过对典型非线性系统的仿真,验证了该方法的有效性和可行性.     

随机非线性系统的输出反馈控制

针对满足线性增长条件的一类随机非线性系统, 本文研究了输出反馈镇定问题. 然而不同于现有的所有文献, 由于线性增长条件中含有不可量测的状态, 引入了一个待定的高增益观测器. 利用反推设计技术, 构造性地给出了一个输出反馈控制器的设计, 通过适当地选取高增益参数, 保证了闭环系统的零解是概率意义下全局渐近稳定的, 输出几乎处处调节于零.     

磁浮列车搭接结构的非线性解耦控制

磁浮列车的搭接结构是一个多输入多输出的非线性系统, 对该系统进行稳定控制是确保磁浮列车可靠性的关键. 本文采用微分几何方法对系统解耦; 采用反馈线性化方法对解耦后的系统进行线性化, 确保了系统的全局稳定性; 对线性化之后的系统采用极点配置的方法进行控制律设计, 使系统的动态性能达到一定的指标; 最后给出了应用此方法的一个实际例子.     

具有区间时变时滞的离散Markov跳变系统鲁棒H∞控制

研究一类具有区间时变时滞的离散时间不确定Ｍａｒｋｏｖ跳变系统的时滞相关鲁棒H_∞ 控制问题．通过构造新的ＬｙａｐｕｎｏｖＫｒａｓｏｖｓｋｉｉ泛函,基于有限和不等式方法设计状态反馈控制器,使得闭环系统在容许不确定性下鲁棒稳定,且对能量有界的输入噪声满足一定输入输出H_∞ 增益．在新控制器存在条件中未引入任何自由变量矩阵,使之可更为有效地求解．基于锥补线性化的迭代算法可有效求解H_∞ 次优控制器．数值算例表明了所提出方法的有效性．     

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. Recommended by Editorial Board member Hyo-Choong Bang under the direction of Editor Hyun Seok Yang. This work was supported by the Korea Research Foundation Grant (MOEHRD) KRF-2005-204-D00002, the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) R0A-2007-000-10017-0 and Engineering Research Institute at Seoul National University. Daewon Lee received the B.S. degree in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical and Aerospace Engineering. He has been a member of the UAV research team at SNU since 2005. His research interests include applications of nonlinear control and vision-based control of UAV. H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley in 1999 and 2001, respectively. From 2002–2004, she was a Postdoctoral Researcher and Lecturer in Electrical Engineering and Computer Science (EECS), University of California, Berkeley (UC Berkeley). From 2004–2009, she was an Assistant Professor in the School of in Mechanical and Aerospace Engineering at Seoul National University (SNU), Seoul, Korea, where she is currently an Associate Professor. Her research interests include applications of nonlinear control theory and artificial intelligence for robotics, motion planning algorithms. Shankar Sastry received the B.Tech. degree from the Indian Institute of Technology, Bombay, in 1977, and the M.S. degree in EECS, the M.A. degree in mathematics, and the Ph.D. degree in EECS from UC Berkeley, in 1979, 1980, and 1981, respectively. He is currently Dean of the College of Engineering at UC Berkeley. He was formerly the Director of the Center for Information Technology Research in the Interest of Society (CITRIS). He served as Chair of the EECS Department from January, 2001 through June 2004. In 2000, he served as Director of the Information Technology Office at DARPA. From 1996 to 1999, he was the Director of the Electronics Research Laboratory at Berkeley (an organized research unit on the Berkeley campus conducting research in computer sciences and all aspects of electrical engineering). He is the NEC Distinguished Professor of Electrical Engineering and Computer Sciences and holds faculty appointments in the Departments of Bioengineering, EECS and Mechanical Engineering. Prior to joining the EECS faculty in 1983 he was a Professor with the Massachusetts Institute of Technology (MIT), Cambridge. He is a member of the National Academy of Engineering and Fellow of the IEEE.     

Control of 1-D parabolic PDEs with Volterra nonlinearities, Part I: Design

Boundary control of nonlinear parabolic PDEs is an open problem with applications that include fluids, thermal, chemically-reacting, and plasma systems. In this paper we present stabilizing control designs for a broad class of nonlinear parabolic PDEs in 1-D. Our approach is a direct infinite dimensional extension of the finite-dimensional feedback linearization/backstepping approaches and employs spatial Volterra series nonlinear operators both in the transformation to a stable linear PDE and in the feedback law. The control law design consists of solving a recursive sequence of linear hyperbolic PDEs for the gain kernels of the spatial Volterra nonlinear control operator. These PDEs evolve on domains T_n of increasing dimensions n+1 and with a domain shape in the form of a “hyper-pyramid”, 0≤ξ_n≤ξ_n−1?≤ξ₁≤x≤1. We illustrate our design method with several examples. One of the examples is analytical, while in the remaining two examples the controller is numerically approximated. For all the examples we include simulations, showing blow up in open loop, and stabilization for large initial conditions in closed loop. In a companion paper we give a theoretical study of the properties of the transformation, showing global convergence of the transformation and of the control law nonlinear Volterra operators, and explicitly constructing the inverse of the feedback linearizing Volterra transformation; this, in turn, allows us to prove L² and H¹ local exponential stability (with an estimate of the region of attraction where possible) and explicitly construct the exponentially decaying closed loop solutions.     

基于网络控制系统的感应电机非线性控制

针对网络控制系统中存在不确定时延的问题,讨论了网络条件下感应电机的建模与控制．首先基于感应电机的非线性数学模型,运用直接反馈化方法将其线性化,然后在线性化的基础上引入具有时延的网络控制系统,建立了感应电机的网络化控制模型．运用李亚普诺夫函数方法,给出了闭环系统渐近稳定的充分条件,基于相应的线性矩阵不等式可行解,可以求解状态反馈控制律．最后,仿真结果验证了该方法的有效性．     

A structural linearization principle for processes

In [11], an induction principle for processes was given which allows one to apply model-checking techniques to parameterized families of processes. A limitation of the induction principle is that it does not apply to the case in which one process depends directly upon a parameterized number of processes, which grows without bound. This would seem to preclude its application to families ofN processes interconnected in a star topology. Nonetheless, we show that if the dependency can be computed incrementally, then the direct dependency upon the parameterized number of processes may be re-expressed recursively in terms of a linear cascade of processes, yielding in effect a linearization of the inter-process dependencies and allowing the induction principle to apply.A previous version of this paper appears in the Proceedings of CAV 1993 (LNCS 697).