LMI-based robust iterative learning controller design for discrete linear uncertain systems

This paper addresses the design problem of robust iterative learning controllers for a class of linear discrete-time systems with norm-bounded parameter uncertainties. An iterative learning algorithm with current cycle feedback is proposed to achieve both robust convergence and robust stability. The synthesis problem of the proposed iterative learmng control （ILC） system is reformulated as a γ-suboptimal H-infinity control problem via the linear fractional transformation （LFT）. A sufficient condition for the convergence of the ILC algorithm is presented in terms of linear matrix inequalities （LMIs）. Furthermore, the linear wansfer operators of the ILC algorithm with high convergence speed are obtained by using existing convex optimization techniques. The simulation results demonstrate the effectiveness of the proposed method.     

Controller design of high order nonholonomic system with nonlinear drifts

A controller design is proposed for a class of high order nonholonomic systems with nonlinear drifts. The purpose is to ensure a solution for the closed-loop system regulated to zero. Adding a power integrator backstepping technique and the switching control strategy are employed to design the controller. The state scaling is applied to the recursive manipulation. The simulation example demonstrates the effectiveness and robust features of the proposed method.     

Neuro-fuzzy generalized predictive control of boiler steam temperature

Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modem power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network （NFGPC） is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained,     

9th International Conference on Control, Automation, Robotics and Vision ICARCV 2006

The 9th International Conference on Control, Automation, Robotics and Vision, ICARCV 2006, will be held in Singapore from 5 - 8 December 2006. The conference is organised by the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore and Institution of Engineers,Singapore. ICARCV focuses on both theory and applications mainly covering the topics of control,automation,robotics and vision. In addition to the technical sessions, there will be invited sessions, tutorials and keynote addresses.     

Nonlinear adaptive decentralized stabilization control for multimachine power systems

This paper presents a decentralized adaptive backstepping controller to dampen oscillations and improve the transient stability to parametric uncertainties in multimachine power systems. The proposed design on the i ^th synchronous generator uses only local information and operates without the need for remote signals from the other generators. The design of the nonlinear controller is based on a modified fourth-order nonlinear model of a synchronous generator, and the automatic voltage regulator model is considered so as to decrease the steady state voltage error. The construction of both the control law and the associated Lyapunov function is systematically designed within the design methodology. A 3-machine power system is used to demonstrate the effectiveness of the proposed controller over two other controllers, namely a conventional damping controller (power system stabilizer) and one designed using the feedback linearization techniques. Recommended by Editorial Board member Gang Tao under the direction of Editor Jae Weon Choi. This work was supported by the Korea Electrical Engineering and Science Research Institute, which is funded by Ministry of Commerce, Industry and Energy. Shan-Ying Li received the B.S. degrees in Computer Science and M.S. degree in Electrical Engineering from Northeast DianLi University, China, in 1997 and 2002, respectively. She obtained the Ph.D. degree in Electrical Engineering from Seoul National University, Korea, in 2008. She is a Post Doctor in North China Electric Power Research Institute, North China Grid Co., Ltd., China. Her research interests are in the areas of advanced control and stability applications on power systems. Sang-Seung Lee received the M.S.E.E. and Ph.D. degrees in Electrical Engineering at Seoul National University. Currently, he is with Power System Research Division of KESRI, Seoul National University, Korea. His interest areas are nonlinear/adaptive control theory, North-East Asia power system interconnection, distributed/small generation, distributed transmission/distribution load flow algorithm, regional/local energy system, PSS (power system stabilizer), and RCM (Reliability Centered Maintenance). Yong Tae Yoon was born in Korea on April 20, 1971. He received the B.S. degree, M.Eng. and Ph.D. degrees from M.I.T., USA in 1995, 1997, and 2001, respectively. Currently, he is an Assistant Professor in the School of Electrical Engineering and Computer Science at Seoul National University, Korea. His special field of interest includes electric power network economics, power system reliability, and the incentive regulation of independent transmission companies. Jong-Keun Park received the B.S. degree in Electrical Engineering from Seoul National University, Seoul, Korea in 1973 and the M.S. and Ph.D. degrees in Electrical Engineering from The University of Tokyo, Japan in 1979 and 1982, respectively. He is currently a Professor of School of Electrical Engineering, Seoul National University. In 1992, he attended as a Visiting Professor at Technology and Policy Program and Laboratory for Electromagnetic and Electronic Systems, Massachusetts Institute of Technology. He is a Senior Member of the IEEE, a Fellow of the IEE, and a Member of Japan Institute of Electrical Engineers (JIEE).     

Nonlinear backstepping ship course controller

A ship, as an object of course control, is characterized by a nonlinear function describing the static maneuvering characteristics. The backstepping method is one of the methods that can be used during the designing process of a nonlinear course controller for ships. The method has been used for the purpose of designing two configurations of nonlinear controllers, which were then used to control the ship course. One of the configurations took dynamic characteristic of a steering gear into account during the designing stage. The parameters of the obtained nonlinear control structures have been tuned to optimise the operation of the control system. The optimisation process has been performed by means of genetic algorithms. The quality of operation of the designed control algorithms has been checked in simulation tests performed on the mathematical model of a tanker. The results of simulation experiments have been compared with the performance of the system containing a conventional proportional-derivative （PD） controller.     

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example. Recommended by Editor Young-Hoon Joo. This work was jointly supported by National Natural Science Foundation of China under Grant 60604010, 90716021, and 90405017 and Foundation of National Laboratory of Space Intelligent Control of China under Grant SIC07010202. Xiong Luo received the Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was a Postdoctoral Fellow in the Department of Computer Science and Technology at Tsinghua University. He currently works as an Associate Professor in the Department of Computer Science and Technology, University of Science and Technology Beijing. His research interests include intelligent control for spacecraft, intelligent optimization algorithms, and intelligent robot system. Zengqi Sun received the bachelor degree from Tsinghua University, Beijing, China, in 1966, and the Ph.D. degree from Chalmers University of the Technology, Gothenburg, Sweden, in 1981. He currently works as a Professor in the Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control of robotics, fuzzy neural networks, and intelligent flight control. Fuchun Sun received the Ph.D. degree from Tsinghua University, Beijing, China, in 1998. From 1998 to 2000, he was a Postdoctoral Fellow in the Department of Automation at Tsinghua University, where he is currently a Professor in the Department of Computer Science and Technology. His research interests include neural-fuzzy systems, variable structure control, networked control systems, and robotics.     

Relaxed stability condition and state feedback H ∞ controller design for T-S fuzzy systems

In this paper, a fuzzy Lyapunov approach is presented for stability analysis and state feedback H _∞ controller design for T-S fuzzy systems. A new stability condition is obtained by relaxing the ones derived in previous papers. Then, a set of LMI-based sufficient conditions which can guarantee the existence of state feedback H _∞ controller for T-S fuzzy systems is proposed. In comparison with the existing literature, the proposed approach not only provides more relaxed stability conditions but also ensures better H _∞ performance. The effectiveness of the proposed approach is shown through two numerical examples. Recommended by Editor Young-Hoon Joo. Xiao-Heng Chang received the B.E. and M.S. degrees from Liaoning Technical University, China, in 1998 and 2004, respectively, and the Ph.D. degree from Northeastern University, China, in 2007. He is currently a Lecturer in the School of Information Science and Engineering, Bohai University, China. His research interests include fuzzy control and robust control as well as their applications. Guang-Hong Yang received the B.S. and M.S. degrees in Northeast University of Technology, China, in 1983 and 1986, respectively, and the Ph.D. degree in Control Engineering from Northeastern University, China (formerly, Northeast University of Technology), in 1994. He was a Lecturer/Associate Professor with Northeastern University from 1986 to 1995. He joined the Nanyang Technological University in 1996 as a Postdoctoral Fellow. From 2001 to 2005, he was a Research Scientist/Senior Research Scientist with the National University of Singapore. He is currently a Professor at the College of Information Science and Engineering, Northeastern University. His current research interests include fault-tolerant control, fault detection and isolation, nonfragile control systems design, and robust control. Dr. Yang is an Associate Editor for the International Journal of Control, Automation, and Systems (IJCAS), and an Associate Editor of the Conference Editorial Board of the IEEE Control Systems Society.     

Observer-based direct adaptive fuzzy control of uncertain nonlinear systems and its applications

A direct adaptive fuzzy control algorithm is developed for a class of uncertain SISO nonlinear systems. In this algorithm, it doesn’t require to assume that the system states are measurable. Therefore, it is needed to design an observer to estimate the system states. Compared with the numerous alternative approaches with respect to the observer design, the main advantage of the developed algorithm is that on-line computation burden is alleviated. It is proven that the developed algorithm can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to a small neighborhood around zero. The simulation examples validate the feasibility of the developed algorithm. Recommended by Editorial Board member Zhong Li under the direction of Editor Young-Hoon Joo. This work is supported by National Natural Science Foundation of China under grant 60674056, 60874056, and the Foundation of Educational Department of Liaoning Province (2008312). Yan-Jun Liu received the B.S. degree in Applied Mathematics from Shenyang University of Technology in 2001. He received the M.S. degree in Control Theory and Control Engineering from Shenyang University of Technology in 2004 and the Ph.D. degree in Control Theory and Control Engineering from Dalian University of Technology, China, in 2007. His research interests include fuzzy control theory, nonlinear control and adaptive control. Shao-Cheng Tong received the B.S. degree in Department of Mathematics from Jinzhou Normal College, China, in 1982. He received the M.S. degree in Department of Mathematics from Dalian Marine University in 1988 and the Ph.D. degree in Control Theory and Control Engineering from Northeastern University, China, in 1997. His research interests include fuzzy control theory, nonlinear control, adaptive control, and system identification etc. Wei Wang received the B.S. degree in Department of Automation from Northeastern University, China, in 1982. He received the M. S. degree in Department of Automation from Northeastern University in 1984 and the Ph.D. degree in Department of Automation from Northeastern University, China, in 1988. His research interests include adaptive predictive control, intelligent control, and production scheduling method etc. Yong-Ming Li received the B.S. degree in Applied Mathematics from Liaoning University of Technology in 2004. He received the M.S. degree in Applied Mathematics from Liaoning University of Technology in 2007. His research interests include fuzzy control theory, nonlinear control and adaptive control.     

自适应模糊PID控制器的设计

目前的工业过程控制中被控对象往往都是非线性、时变的系统,常规PID控制对于这样系统的控制效果不是很理想。为此,提出将模糊技术与PID控制相结合的控制方式,设计出自适应模糊PID控制器。利用Matlab中模糊逻辑控制工具箱设计模糊控制器,在Simulink环境中实现了该自适应模糊PID控制器的仿真。仿真结果表明,该模糊PID控制具有控制灵活、响应快和适应性能强的优点。     

Power Efficient Sub-Array in Reconfigurable VLSI Meshes

Given an m×n mesh-connected VLSI array with some faulty elements, the reconfiguration problem is to find a maximum-sized fault-free sub-array under the row and column rerouting scheme. This problem has already been shown to be NP-complete. In this paper, new techniques are proposed, based on heuristic strategy, to minimize the number of switches required for the power efficient sub-array. Our algorithm shows that notable improvements in the reduction of the number of long interconnects could be realized in linear time and without sacrificing on the size of the sub-array. Simulations based on several random and clustered fault scenarios clearly reveal the superiority of the proposed techniques.     

模糊自适应PID参数自整定控制器的研究

当控制系统中的被控对象存在纯滞后、时变或非线性等复杂因素时,普通的PID控制器的控制效果很难达到较好的控制效果,针对这一问题,应用模糊控制和自适应控制的知识,设计了模糊自适应PID参数自整定控制器,此控制器的比例系数、积分系数和微分系数可根据模糊推理规则进行在线调整。仿真结果表明,该控制方法提高了系统的动、静态特性,使该系统具有较好的鲁棒性。     

Stable nonlinear controller design for a Takagi-Sugeno fuzzy model

This paper proposes another adaptive control scheme for nonlinear systems using a Takagi-Sugeno fuzzy model. Takagi-Sugeno fuzzy models have been widely used to identify the structures and parameters of unknown or partially known plants, and to control nonlinear systems. This scheme shows a good approximation capability by the fuzzy blending of local dynamics. Since a Takagi-Sugeno fuzzy model is a nonlinear system in nature, and its parameters are not linearly parameterized, it is difficult to design an adaptive controller using conventional design methods for adaptive controllers which are derived from linearly parameterized systems. In this paper, the functional form of the local dynamics are assumed to be known, but the corresponding parameters are unknown. This additional information about system nonlinearity makes it possible to design an adaptive controller for a nonlinearly parameterized system. The control law is similar to that of a conventional adaptive control technique, while its parameter-update rule is based on the local search method. A parameter-update law is derived so that the time-derivative of the Lyapunov function is negative in the region of interest. Simulation results have shown that this adaptive controller is capable of a good performance. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

An improved control algorithm for high-order nonlinear systems with unmodelled dynamics

In this paper, we consider a class of high-order nonlinear systems with unmodelled dynamics from the viewpoint of maintaining the desired control performance （e,g., asymptotical stability） and reducing the control effort. By introducing a new reseating transformation, adopting an effective reduced-order observer, and choosing an ingenious Lyapunov function and appropriate design parameters, this paper designs all improved output-feedback controller. The output-feedback controller guarantees the globally asymptotieal stability of the closed-loop system. Subsequently, taking a concrete system for an example, the smaller critical values for gain parameter and resealing transformation parameter are obtained to effectively reduce the control effort.     

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.     

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.     

一种新的自适应模糊滑模控制器设计方法

对一类非线性系统提出一种新的自适应模糊滑模控制器设计方法。将自适应模糊控制与滑模控制有效地结合在一起,先用滑模控制使跟踪误差进入边界层内,然后启动自适应模糊控制器。该控制器可消除滑模控制器中出现的抖振,并可在存在模糊逻辑系统逼近误差情况下使系统跟踪误差小于预先给定的任意常数。仿真算例验证了所提出方法的有效性。     

Direct adaptive fuzzy control for nonlinear systems with time-varying delays

This paper focuses on the problem of direct adaptive fuzzy control for nonlinear strict-feedback systems with time-varying delays. Based on the Razumikhin function approach, a novel adaptive fuzzy controller is designed. The proposed controller guarantees that the system output converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. Different from the existing adaptive fuzzy control methodology, the fuzzy logic systems are used to model the desired but unknown control signals rather than the unknown nonlinear functions in the systems. As a result, the proposed adaptive controller has a simpler form and requires fewer adaptation parameters.     

船舶航向模型参考模糊自适应控制器的设计

本文介绍了模型模糊自适应控制器的结构，调节机理。针对船舶航向控制，设计了参考模型模糊自适应控制器，并进行了仿真。与普通模糊控制器控制效果进行了比较，仿真表明参考模型模糊自适应控制有良好的控制性能。