Sharif Digital Repository

Synthetic inertial frequency response has attracted increasing attention due to the paradigm shift in power generation from synchronous generators toward wind turbines. This paper presents a novel nonlinear controller to enable inertial response of a variable speed wind turbine (VSWT). The VSWT has a nonlinear model which cannot efficiently be dealt with by linear control techniques as it may experience large deviations from its operating point during the frequency support period. To design the controller, a nonlinear model for the wind turbine together with power system is utilized. The output power of the wind turbine is expressed in terms of the state variables, and its exact Taylor... 

In this research, the nonlinear elastic behavior of human extensor apparatus was investigated. To this goal, firstly the best material parameters of hyperelastic strain energy density functions consisting of the Mooney–Rivlin, Ogden, invariants, and general exponential models were derived for the simple tension experimental data. Due to the significance of stress response in other deformation modes of nonlinear models, the calculated parameters were used to study the pure shear and balance biaxial tension behavior of the extensor apparatus. The results indicated that the Mooney–Rivlin model predicts an unstable behavior in the balance biaxial deformation of the extensor apparatus, while the... 

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... 

Influences of the tip mass, excitation mode of Frequency Modulated Atomic Force Microscope (FM-AFM) on the resonance frequency shift in force modulation (FM) mode are studied. Governing equations of motion are determined based on Timoshenko beam model with concentrated end mass. Approach point and base amplitude are set such that the FM-AFM remains just in FM mode. Either the linearized and nonlinear Derjaguin-Muller-Toporov (DMT) model are investigated. Then frequency shifts are determined for various interaction force regimes. It is showed the effect of tip mass on frequency shift is significant even for small tips. Nonlinear model shows lower frequency shifts in comparison with linearized... 

In this paper, we propose a new derivative-free preconditioned conjugate gradient method in order for solving large-scale square and under-determined nonlinear systems of equations. The proposed method is also equipped with a relaxed nonmonotone line search technique. Under some suitable assumptions, the global convergence property is established. Numerical results on some square and under-determined test systems show the efficiency and effectiveness of the new method in practice. An application of the new method for solving nonlinear integro-differential equations is also provided. © 2016 Elsevier Ltd  

This paper deals with the cancer treatment scheduling via observer-based feedback control of a tumor growth biodynamical model. An experimental nonlinear model is utilized to design a feedback controller. This model was validated by a pharmacological study on xenograft models obtained by transplantation of colon carcinoma cell lines in athymic mice. Input-output feedback linearization approach is used to linearize the nonlinear model of the system and design a control law. It is assumed that tumor weight can be measured in definite intervals. So a continuous-discrete observer is designed to estimate states of the system based on discrete sampled data which are tumor weight in this case. To... 

Purpose: The purpose of this paper is to design an adaptive nonlinear controller for a nonlinear system of integrated guidance and control. Design/methodology/approach: A nonlinear integrated guidance and control approach is applied to a homing, tail-controlled air vehicle. Adaptive backstepping controller technique is used to deal with the problem, and the Lyapanov theory is used in the stability analysis of the nonlinear system. A nonlinear model of normal force coefficient is obtained from an existing nonlinear model of lift coefficient which was validated by open loop response. The simulation was performed in the pitch plane to prove the benefits of the proposed scheme; however, it can... 

Kinetic modeling of CO hydrogenation over Fe-Co-Mn catalyst was investigated; a ternary catalyst was prepared using incipient wetness impregnation. The kinetic parameters were determined from experiments carried out in a fixed bed micro-reactor under the following process conditions: T = 523.15-533.15 K, P = 1-10 bar, H2/CO = 1/1-3/1 and space velocity = 4500 h-1. Seventeen rate expressions were derived from five different mechanisms according to Langmuir-Hinshelwood-Hougen-Watson (LHHW) and Eley-Rideal (ER) mechanisms and were tested against the experimental data. Non-linear regression method was used to estimate different kinetic parameters; according to the obtained experimental results... 

Various systems with nonlinearity can be modeled by utilizing uncertain linear-in-parameter models. In this paper, the uncertain parameters are in the form of Z-number coefficients. Fuzzy equations are utilized to represent the models of the uncertain nonlinear systems. The solutions associated with fuzzy equations are considered to be controllers while the desired references are outputs. The existence condition associated with the solution is laid down. Two various structure of neural networks are applied for approximating solutions of fuzzy equations with Z-number coefficients. The suggested techniques are validated by implementing an example. © 2018 The Authors. Published by Elsevier B.V  

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... 

This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on simplex technique to find and track the best estimation. The vertices of the simplex search the state space dynamically in a similar scheme to the optimization algorithm, known as Nelder-Mead simplex. The parameters of the proposed filter are tuned, using an information visualization technique to identify the optimal region of the parameters space. The visualization is performed using the concept of parallel coordinates. The proposed filter is applied to estimate the state... 

This paper presents a robust model predictive control algorithm with a time-varying terminal constraint set for systems with model uncertainty and input constraints. In this algorithm, the nonlinear system is approximated by a linear model where the approximation error is considered as an unstructured uncertainty that can be represented by a Lipschitz nonlinear function. A continuum of terminal constraint sets is constructed off-line, and robust stability is achieved on-line by using a variable control horizon. This approach significantly reduces the computational complexity. The proposed robust model predictive controller with a terminal constraint set is used in tracking set-points for... 

Influences of the tip mass, excitation mode of Frequency Modulated Atomic Force Microscope (FM-AFM) on the resonance frequency shift in force modulation (FM) mode are studied. Governing equations of motion are determined based on Timoshenko beam model with concentrated end mass. Approach point and base amplitude are set such that the FM-AFM remains just in FM mode. Either the linearized and nonlinear Derjaguin-Muller-Toporov (DMT) model are investigated. Then frequency shifts are determined for various interaction force regimes. It is showed the effect of tip mass on frequency shift is significant even for small tips. Nonlinear model shows lower frequency shifts in comparison with linearized... 

In this paper, a new form of generalized nonsingular fast terminal sliding mode control approach is proposed to provide the finite time tracking in connected chain of double integrator nonlinear systems subjected to additive bounded unknown uncertainties, disturbances, and internal interactions. The proposed approach presents an adjustable finite time for achieving the tracking goal which is a summation of two separate tunable times including finite reaching time and finite settling time. Tuning of the total finite time is done by adjusting arbitrary parameters in the control inputs and sliding surfaces. The high frequency switching of the control method is removed by applying a second order... 

In this paper, a learning algorithm is developed for Dynamic Plastic Continuous Neural Networks (DPCNNs) to improve their learning of highly nonlinear time dependent problems. A DPCNN is comprised of a base medium, which is nonlinear and plastic, and a number of neurons that are attached to the base by wire-like connections similar to perceptrons. The information is distributed within DPCNNs gradually and through wave propagation mechanism. While a DPCNN is adaptive due to its connection weights, the material properties of its base medium can also be adjusted to improve its learning. The material of the medium is plastic and can contribute to memorizing the history of input-response similar... 

Due to the complexity of the human spinal motion segments, the intervertebral joints are often simulated in the musculoskeletal trunk models as pivots thus allowing no translational degrees of freedom (DOFs). This work aims to investigate, for the first time, the effect of such widely used assumption on trunk muscle forces, spinal loads, kinematics, and stability during a number of static activities. To address this, the shear deformable beam elements used in our nonlinear finite element (OFE) musculoskeletal model of the trunk were either substantially stiffened in translational directions (SFE model) or replaced by hinge joints interconnected through rotational springs (HFE model). Results... 

Representing nonlinear systems by linear models along with structured or unstructured uncertainties and applying robust control strategies could reduce the computational complexity in comparison with implementing the nonlinear model predictive controllers. In this paper design of robust model predictive controllers which are based on special classes of nonlinear systems representations called Wiener and Hammerstein are presented. The proposed algorithms approximate the nonlinear systems by uncertain linear models and reduce online the computational demands in the control implementation. The advantages of the proposed approaches are illustrated by two examples  

We propose a new image recovery method to improve the resolution in microwave imaging applications. Scattered field data obtained from a simplified breast model with closely located targets is used to formulate an electromagnetic inverse scattering problem, which is then solved using the Distorted Born Iterative Method (DBIM). At each iteration of the DBIM method, an underdetermined set of linear equations is solved using our proposed sparse recovery algorithm, IMATCS. Our results demonstrate the ability of the proposed method to recover small targets in cases where traditional DBIM approaches fail. Furthermore, in order to regularize the sparse recovery algorithm, we propose a novel... 

This paper presents an investigation of the dynamics of two coupled non-identical FitzHugh-Nagumo neurons with delayed synaptic connection. We consider coupling strength and time delay as bifurcation parameters, and try to classify all possible dynamics which is fairly rich. The neural system exhibits a unique rest point or three ones for the different values of coupling strength by employing the pitchfork bifurcation of non-trivial rest point. The asymptotic stability and possible Hopf bifurcations of the trivial rest point are studied by analyzing the corresponding characteristic equation. Homoclinic, fold, and pitchfork bifurcations of limit cycles are found. The delay-dependent stability... 

This paper considers the output feedback adaptive controller design problem for a class of discrete-time nonlinear systems in output feedback form with unknown control directions and preceded by unknown hysteresis. The problem of lacking in a-priori knowledge on the control directions and unknown hysteresis are solved by using the discrete Nussbaum gain and Prandtl-Ishlinskii model, respectively. The system is transformed into the form of a nonlinear auto regressive moving average (NARMA) model to construct an output feedback control. To overcome the noncausal problem in the control design, future output prediction laws and parameter update laws with the dead-zone technique are constructed...