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.     

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.     

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).     

A nonlinear geometric approach to power system excitation control and stabilization

In this paper, a unified approach to the design of a nonlinear excitation controller/power system stabilizer for a synchronous generator/infinite bus power system is presented. The approach is based on a form of state feedback linearization, known as input–output feedback linearization, which provides an exact semi-global state transformation that is valid for a large class of operating points of the power system. With this transformation, the terminal voltage becomes a linear function of the control input. The excitation controller/power system stabilizer is then synthesized by using linear controller design techniques. The controller is proven to provide small signal stability and to provide local asymptotic tracking of admissible constant reference signals for a large class of operating points. A procedure is given to tune the controller gains to provide significant damping of the power angle oscillations.     

Robust adaptive control for greenhouse climate using neural networks

This paper presents a general framework for robust adaptive neural network (NN)‐based feedback linearization controller design for greenhouse climate system. The controller is based on the well‐known feedback linearization, combined with radial basis functions NNs, which allows the feedback linearization technique to be used in an adaptive way. In addition, a robust sliding mode control is incorporated to deal with the bounded disturbances and the approximation errors of NNs. As a result, an inherently nonlinear robust adaptive control law is obtained, which not only provides fast and accurate tracking of varying set‐points, but also guarantees asymptotic tracking even if there are inherent approximation errors. Copyright © 2010 John Wiley & Sons, Ltd.     

空间电磁对接控制问题

航天器空间电磁对接能避免诸如推进剂消耗、羽流污染和对接冲击等现有基于推力器对接所固有的缺陷,应用前景广阔.空间电磁对接控制目前国内外还没有有效研究,论文主要针对此问题开展初步研究工作.论文首先重点分析空间电磁对接特性及其主要控制问题,然后采用反馈线性化方法对地面二维电磁对接控制模型进行线性化处理、以及基于线性化模型应用Kalman滤波方法估计电磁对接运动状态,最后采用二阶稳定性方法设计控制律并进行仿真分析.理论研究和仿真分析表明:航天器空间电磁对接是可行的,电磁对接非线性模型可以采用反馈线性化方法实现精确线性化,基于线性化模型的二阶稳定性方法满足控制律设计要求.     

Welding current and arc voltage control in a GMAW process using ARMarkov based MPC

A predictive functional controller based on ARMarkov model structure has been designed to control welding current and arc voltage in a GMAW process. The closed loop system performance is investigated through computer simulations and is compared by those achieved from implementing two commonly used controllers i.e. PI and feedback linearization based PID. The local stability of the closed loop system is analyzed in the presence of uncertainties in the linearized model of the process as well as the control parameters. Finally it is shown that the proposed controller performs like a PI controller along with a pre-filter compensator.     

Robust adaptive fuzzy logic power system stabilizer

This paper introduces a robust adaptive fuzzy controller as a power system stabilizer (RFPSS) used to damp inter-area modes of oscillation following disturbances in power systems. In contrast to the IEEE standard multi-band power system stabilizer (MB-PSS), robust adaptive fuzzy-based stabilizers are more efficient because they cope with oscillations at different operating points. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, components that ensure robust and adaptive performance are included in the control law to compensate for modelling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearities. The second system is an adaptive one that compensates for modelling errors. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.     

反馈线性化在液压型风力发电机组功率追踪中的应用

以液压型风力发电机组为研究对象,为使功率追踪的过程平稳,研究机组的最佳功率追踪控制方法.本文利用反馈线性化方法解决系统非线性问题,以液压系统压力为控制输出,设计最佳功率追踪控制器,并提出一种反馈线性化方法的工程应用解决方案,即结合传统PID控制解决反馈线性化工程应用中依赖模型参数精度的问题.依托30 k VA液压型风力发电机组半物理仿真实验台进行仿真和实验研究,验证了该方法的可行性,为机组进一步研究奠定理论与实验基础.