Sharif Digital Repository

This paper studies and compares three nonadaptive (bidirectional, unidirectional, and sliding mode) and two adaptive (active control and backstepping) synchronization methods on the synchronizing of four pairs of identical chaotic systems (Chua's circuit, Rössler system, Lorenz system, and Lü system). Results from computer simulations are presented in order to illustrate the effectiveness of the methods and to compare them based on different criteria. © 2005 Elsevier Ltd. All rights reserved  

This paper presents chaos synchronization between two different chaotic systems when the parameters of the drive and response systems are fully unknown and uncertain. Based on Lyapunov stability theory, an adaptive control law is designed such that the two different chaotic systems are to be synchronized. The proposed technique is applied to achieve chaos synchronization for the Chen and Lorenz dynamical systems. Numerical simulations are implemented to verify the results. © 2005 IEEE  

The use of adaptive feedforward controllers has proven to be a very successful strategy for controlling noise and vibration in a variety of applications. One reason is that the feedforward controller is an open loop controller, which can be designed to cancel the undesired noise in one position with any accuracy. However, the feedforward controller requires an input signal, called a reference signal, correlated to the noise source. As a consequence, a single reference controller can only reduce noise radiated from a single noise source. In many applications, there is a need to attenuate noise produced by several noise sources. In this paper, three different structures, single, modulating and... 

This paper addresses design of an observer-based adaptive fuzzy controller for a class of single-input-single-output (SISO) nonlinear systems with unknown dynamics subject to input nonlinearity and unknown direction. The proposed controller is singularity free. A high-gain observer is designed to estimate the unmeasured states, and the Lipschitz condition for proving boundedness of the estimated states is relaxed. The Nussbaum function is used to handle the unknown virtual control directions and the backstepping technique has been applied for controller design. It is proved that all closed loop signals are semi-globally uniformly ultimately bounded (SGUUB) and the output tracking error... 

In this paper, model reference control of a fractional order system has been discussed. In order to control the fractional order plant, discrete-time approximation methods have been applied. Plant and reference model are discretized by Grünwald-Letnikov definition of the fractional order derivative using "Short Memory Principle". Unknown parameters of the fractional order system are appeared in the discrete time approximate model as combinations of parameters of the main system. The discrete time MRAC via RLS identification is modified to estimate the parameters and control the fractional order plant. Numerical results show the effectiveness of the proposed method of model reference adaptive... 

Among innovations and improvements that occurred in the past two decades on the techniques and tools used for statistical process control (SPC), adaptive control charts have shown to substantially improve the statistical and/or economical performances. Variable sampling intervals (VSI) control charts are one of the most applied types of the adaptive control charts and have shown to be faster than traditional Shewhart control charts in identifying small changes of concerned quality characteristics. While in the designing procedure of the VSI control charts the data or measurements are assumed independent normal observations, in real situations the validity of these assumptions is under... 

In this paper, an adaptive fuzzy temperature controller is proposed for a class of continuous stirred tank reactors (CSTRs) based on input-output feedback linearization. Since for control implementation concentrations of all species are needed, based on the observability concept, a fuzzy logic system is used to estimate the concentration dependent terms and other unknown system parameters in the control law, using temperature measurements. It has been shown that the H∞ tracking control performance with a prescribed attenuation level is achieved, by using the proposed controller. Finally the effectiveness of the proposed controller has been demonstrated by applying it to a benchmark chemical... 

An increasing number of different applications face the challenge of providing end-to-end quality of service (QoS) support such as bandwidth, delay, jitter, and packet loss. In this paper, we have focused on DiffServ architecture to improve its accuracy. We proposed a new algorithm, called Adaptive Model Predictive Control Scheduler (AMPCS), to schedule differentiated buffers in routers, using Adaptive Model Predictive Control as the controller. AMPCS regulates the service rates of aggregated traffic classes dynamically in a way that some constraints on proportional delay or absolute delay can be guaranteed. Our contribution is to apply a model predictive controller to the scheduling problem... 

In this paper an indirect adaptive control algorithm is proposed to stabilize the fixed points of discrete chaotic systems. It is assumed that the functionality of the chaotic dynamics is known but the system parameters are unknown. This assumption is usually applicable to many chaotic systems, such as the Henon map, logistic and many other nonlinear maps. Using the recursive-least squares technique, the system parameters are identified and based on the feedback linearization method an adaptive controller is designed for stabilizing the fixed points, or unstable periodic orbits of the chaotic maps. The stability of the proposed scheme has been shown and the effectiveness of the control... 

In this paper, adaptive model predictive control is applied to schedule differentiated buffers in routers. The proposed algorithm, adaptive model predictive control scheduler (AMPCS), dynamically regulates the service rates of aggregated traffic classes. This algorithm guarantees some required constraints on proportional or absolute delay. The control parameters and the way they are adjusted as well as the problems of implementing the controller at high data rates are investigated. Theoretical analysis and numerical simulations demonstrate stability of AMPCS and its acceptable quality of service differentiations at core routers while maintaining end to end delay constraints. © 2007 Elsevier... 

The laser powder deposition (LPD) process is an advanced material processing technique with many applications. Despite this fact, reliable and accurate control schemes have not yet been fully developed for the process. In this paper, identification of the LPD process is examined to find a more accurate model to predict and control the height of clad in real time. The model is adaptive single inputsingle output (SISO) and its structure is very similar to the Hammerstein model when the effective power (a function of laser power and velocity) is selected as the input and the clad height as the output. Weighted extended recursive least square (WERLS) is adopted to simultaneously estimate the... 

An adaptive fuzzy sliding mode control scheme is presented in this article. Despite the advantages of sliding mode control design for uncertain dynamic systems, classical sliding mode control has a major problem in the form of chattering, produced by the rapid switching used to conduct the state trajectories toward the sliding surfaces. A variety of methods are used to achieve chattering-free motion by smoothing out the switching functions; a fuzzy method is introduced in this article. In the proposed approach, the switching functions are replaced by adaptive fuzzy control signals such that the Lyapunov stability conditions are satisfied. This adaptive fuzzy controller can improve the... 

A simple and systematic approach is developed for modeling and adaptive control of an unknown (or uncertain) chaotic system of the form x(n) = f(X) + g(X)u, using only input-output data obtained from the underlying dynamic system. Two different fuzzy identification methods, i.e. least-squares and gradient descent, are used for identifying the unknown functions f (X) and g (X). Based on the fuzzy modeling, an adaptive controller is devised, which works through sliding mode method. The presented procedure is illustrated by using the chaotic system-modified Duffing's equation as an example, on which simulation results demonstrate the effectiveness of the proposed adaptive algorithm. Copyright ©... 

An expanding flight envelope in the landing phase of a typical jet transport aircraft in presence of strong wind shears using a learning capable control system (LCCS) is investigated. The idea stems from human beings functional architecture that gives them the ability to do more as they age and gain more experience. With the knowledge that classical controllers lack sufficient generality to cope with nonlinear as well as uncertain phenomenon such as turbulent air, the focuse is on different types of intelligent controllers due to their learning and nonlinear generalization capabilities as candidates for the landing flight phase. It is shown that the latter class of controllers could be used... 

In this paper, an approach for adaptive synchronization of uncertain chaotic systems is proposed using adaptive active control. According to the Lyapunov stability theorem, an adaptive control law is derived to make the states of two identical chaotic systems asymptotically synchronized. Simulation results are presented to show the effectiveness of the proposed method. © 2006 ICASE  

In this paper we propose a new paradigm for a Differential Service (DiffServ) network consisting of two-color marking at the edges of the network using TSW2CM coupled with self tuning adaptive controller (STAC) in the core. This scheme exploits online estimates of the network parameters in the controller parameters adjustment. The performance demonstrates that the new scheme is able to admit traffic fairly and achieve edge-to-edge QoS under heavy traffic conditions and network state changes. We illustrate our results using ns-2 simulations and demonstrate the practical impact of self tuning control on managing queue utilization and delay. © 2006 ICASE  

Autonomous traffic control systems are large-scale systems with critical goals. To satisfy expected properties, these systems adapt themselves to possible changes in their environment and in the system itself. The adaptation may result in further changes propagated throughout the system. For each change and its consequent adaptation, assuring the satisfaction of properties of the system at runtime is important. A prominent approach to assure the correct behavior of these systems is verification at runtime, which has strict time and memory limitations. To tackle these limitations, we propose Magnifier, an iterative, incremental, and compositional verification approach that operates on an... 

This article addresses an adaptive backstepping control design for uncertain fractional-order nonlinear systems in the strict-feedback form subject to unknown input quantization, unknown state-dependent control directions, and unknown actuator failure. The system order can be commensurate or noncommensurate. The total number of failures is allowed to be infinite. The Nussbaum function is used to deal with the problem of unknown control directions. Compared with the existing results, the control gains can be functions of states and the knowledge of quantization parameters and characteristics of the actuator failure are unknown. By applying the backstepping control approach based on the... 

In this paper, an adaptive multi-model controller based on CMAC neural networks (AMNNC) is developed for uncertain nonlinear MIMO systems. AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks is used to approximate the system nonlinearity for a given task. The proposed control scheme is applied to control a robotic manipulator, where some varying tasks are repeated but information on the load is not defined; it is unknown and varying. It is shown how the proposed controller is effective because of its capability to memorize the control skill for each task using... 

In this paper an identification method which can estimate the unknown parameters of a general nonlinear system based on three techniques (gradient, least-squares and rapid identification) has been developed. The stability of the proposed schemes has been shown using the Lyapunov stability theorem. The properties of each identification technique have been discussed briefly. Open loop identification of the Lorenz chaotic system is presented to show the effectiveness of the proposed approach. To illustrate the efficiency of the identification method for control purposes, it has been applied for controlling the well-known Lorenz system. By exploiting the property of the system a novel...