Sharif Digital Repository

Design parameters selection in the multimodel adaptive control based on switching and tuning will be investigated. Some design parameters like number of fixed and adaptive models, forgetting factor and minimum time delay between switchings will be considered. A recently developed parameter adaptation algorithm based on closed-loop output error will be compared with the classical least-squares prediction error algorithm in the multimodel adaptive control. The effects of these parameters on the performance in tracking and in regulation of a flexible transmission system will be studied via several simulation examples  

PID controllers have been used for many years in industry and if a controller is well tuned, its performance is acceptable for many industrial processes. When the operating point is changed, due to nonlinear behavior of most processes, the controller should be retuned. In this regard, several self-tuning PID controllers are proposed in the literature. In this paper, four such algorithms are compared through simulation and experimental studies. In the simulation study, the effects of factors such as process pole locations, non-minimum phase behavior and model changes on the performance of the schemes are investigated. Simulation and experimental results demonstrate that one of the schemes... 

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... 

This paper presents an adaptive controller to achieve consensus tracking for the fractional-order linear time invariant swarm systems in which the matrices describing the agent dynamics and the interactive dynamics between agents are unknown. This controller consists of two parts: an adaptive stabilizer and an adaptive tracker. The adaptive stabilizer guarantees the asymptotic swarm stability of the considered swarm system. Also, the adaptive tracker enforces the system agents to track a desired trajectory while achieving consensus. Numerical simulation results are presented to show the effectiveness of the proposed controller. Copyright  

Human posture control is a complex issue in biomechanics. Human body is unstable without any controller. The stabilization of the body is achieved by the activation of the muscles and creating the joint torques. In this paper, human body in upright standing position has been modeled using an inverted double pendulum. Since the body parameters are different among the individuals, it is assumed that these parameters are not known exactly and are uncertain. An adaptive controller based on the inverse dynamics in addition to parameter adaptation law has been designed. The simulation of the system using this controller shows the effectiveness of the proposed method in controlling the human... 

In this paper a new control strategy for adaptive attitude control of multivariable satellite system has been presented. The approach is based on radial basis function neural network (RBFNN). By using four reaction wheels and Modified Rodrigues Parameters (MRPs) for attitude representation, the attitude dynamic of satellite has been considered. The Lyapunov stability theory has been used to achieve a stable closed loop system. Also to enhance the robustness of the controller, the RBF neural network has been employed to estimate the model base terms in control law. The control objective is the plant to track a reference model. Simulation results illustrate the performance of the on-line... 

The single-reference/multiple-output active noise control (ANC) of the accurate physical model of an acoustic duct system, developed earlier by the authors [1] is investigated. An adaptive feedforward algorithm is developed that minimizes a generic cost function consisting of the p-norm of the error vector. A computer model of a multi-channel ANC system, with the tonal and sweep sine input signals, is established, and the results for different single-input/single-output (SISO) configurations (error microphone and secondary source locations) of the ANC system are compared. The dynamic response of the single-reference/multiple-output ANC systems, using the Minimax and MEFXLMS algorithms, is... 

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  

In this paper, a new model reference adaptive controller (MRAC) for uncertain switched linear systems is developed. A class of uncertain switched linear systems with parametric mismatched and input matched uncertainties, control input effectiveness and unmodeled dynamics is studied in this paper. The difference in input matrix for different switching modes which means the input channel degradation is investigated through this paper. By using common Lyapunov function method and developing new linear matrix inequality based sufficient conditions, uniform ultimate boundedness of the reference tracking error is guaranteed by switched MRAC and by using a novel nonlinear controller term, the size... 

In this paper, synchronization of chaotic systems with unknown parameters and unmeasured states is investigated. Two nonidentical chaotic systems in the framework of a master and a slave are considered for synchronization. It is assumed that both systems have uncertain dynamics, and states of the slave system are not measured. To tackle this challenging synchronization problem, a novel neural network-based adaptive observer and an adaptive controller have been designed. Moreover, a neural network is utilized to approximate the unknown dynamics of the slave system. The proposed method imposes neither restrictive assumption nor constraint on the dynamics of the systems. Furthermore, the... 

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  

Four nonlinear Model Reference Adaptive Impedance Controllers are introduced, tested, and compared for the control of the robot impedance with uncertainties in model parameters. The intended application area is human-robot interactions (HRI), particularly rehabilitation robots and haptic interfaces. All of the controllers make the closed-loop dynamics of the robot similar to the reference impedance model besides providing asymptotic tracking of the impedance model for the robot end-effector in Cartesian coordinates. The tracking and global stability are proved using Lyapunov stability theorem. Based on the simulations and experiments on a two-DOFs robot, the effectiveness of the proposed... 

In this article, design of an adaptive control scheme for a class of uncertain single-input single-output systems in strict feedback form via a backstepping technique has been proposed. It is assumed that system output and its derivatives are available. By virtue of the observability concept, it is shown that for this class of systems there exists a one-to-one map, which maps output and its derivatives to system states. By means of this mapping and using linearly parametrised approximators, such as fuzzy logic systems or neural networks, the uncertain nonlinear dynamics and unavailable states are estimated. The proposed adaptive controller guarantees that the closed-loop system is uniformly... 

A comparison of three common controllers for stabilising a vertical take-off and landing air vehicle is presented. RMIT is a small sized tail-sitter ducted fan air vehicle with a particular configuration layout, multiple control surfaces, low weight, and high-speed flight capability. The main problem here is control effectiveness at low flight speeds and transition manoeuvres because of the inherent instability. In the current study, a comprehensive nonlinear model is firstly developed for RMIT, followed by a validation process. Subsequently, linear, adaptive and model predictive controllers are designed in vertical flight. Based on the simulation results, it is shown that the linear... 

In this paper, a nonlinear adaptive control method is presented for the treatment of the Hepatitis B Virus (HBV) infection. Nonlinear dynamics of the HBV, modeling uncertainties and three state variables (the numbers of uninfected and infected cells and free viruses) are taken into account. The proposed control law is designed for the antiviral drug input such that the number of free viruses and consequently the number of infected cells decrease to the desired values. An adaptation law is also presented to overcome modeling uncertainties by updating estimations of the system parameters during the treatment period. The stability of the process and convergence to desired state values are... 

A nonlinear robust adaptive sliding mode admittance controller is proposed for exoskeleton rehabilitation robots. The proposed controller has robustness against uncertainties of dynamic parameters using an adaptation law. Furthermore, an adaptive Sliding Mode Control (SMC) scheme is employed in the control law to provide robustness against disturbances (non-parametric uncertainties) with unknown bounds. For this purpose, another adaptation law is defined for the variation of the SMC gain. The proposed scheme is augmented with an admittance control method to provide the patient with compliance during interaction with the rehabilitation robot. The stability of the proposed controller and the...