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In this paper, the trajectory tracking problem for a wheeled mobile robot in the presence of kinematic and dynamic uncertainties is considered. A nonlinear control approach based on lyapunove stability theory is proposed for this problem. Uncertainties are modeled as lumped disturbances and are estimated by a generalized disturbance observer using Linear Matrix Inequalities(LMI). These estimates are used for control laws design for compensating disturbances. Simulation results show the effectiveness of the proposed method in the presence of uncertainties generated by sliding velocity.

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The control strategy in hybrid electric vehicles (HEV) determines the energy management between an internal combustion (IC) engine and an electric machine to generate the power required to drive the vehicle. In this study, the parameters of an electric assist control strategy of HEV, which is used in ADVISOR software, are optimized by minimizing the fuel consumption while maintaining the engine emissions below the Euro3 standard. In this way, a parallel HEV has been simulated in two drive cycles and the optimal parameters of controller are obtained by the genetic algorithm (GA). The simulation results show that by optimizing the control parameters, the fuel consumption decreases while satisfying the Euro3 standards and other constraints in various drive cycles. Also, different values for optimal parameters have been extracted. This indicates that the optimal parameters of the controller are dependent on the drive cycle.

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Optical interconnects as appropriate alternatives for electrical interconnects in the computer chips and boards can be realized by CMOS-based integrated silicon photonics. Dielectric slot waveguide, as one of the newest optical waveguide structures, can form the infrastructure of the passive and active components in these integrated circuits. The passive components have the linear behavior. In order to realize the all-optical active components such as laser, amplifier, and modulator we can use the nonlinear effects in the silicon photonics waveguides. On the other hand, Si-nc:SiO2 as a new material, has a stronger nonlinear property than Si. The results of the full-wave analyzes of the slot waveguide in the linear and nonlinear regimes show that the slot region of this waveguide can be filled with the Si-nc:SiO2 and also realize a high optical intensity. Therefore, this waveguide intensifies the nonlinear behaviors by two factors.

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Blind source separation is the technique that anyone can separate the original signals from their mixtures without any knowledge about the mixing process, but using some statistical properties of original source signals. Independent component analysis is a statistical method expressed as a set of multidimensional observations that are combinations of unknown variables which are assumed to be statistically independent with respect to each other. In this paper we will use the nonlinear autcorrelation function as an object function to separate the source signals from the noisy mixing signals. Also we apply the wavelet transform in our proposed algorithm. Maximization of the object function in wavelet domain using the LMS algorithm will be obtained the coefficients of a linear filter which separate the source signals with high SNR. To calculate the performance of the proposed algorithm, two parameters of Performance Index and Signal to Noise and Interference Ratio will be used. To test the proposed algorithm, we will use Inovation Gaussian signals, Speech signals and ECG signals. Finally level of wavelet decomposition effects will be consider on the obtained results. It will be shown that the proposed algorithm gives better results than the other methods such as NoisyNA method that has been proposed by Shi.

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Generally nonlinear modelling of aerospace system has uncertainty in model parameters and also in real situation different disturbances are applied to system. In spite of these uncertainties and disturbances, autopilot control system should be guarantee stability and desired performance of system. The conditions such as fast response, low tracking error, system robustness must be considered in autopilot design. In this paper, a new method is suggested to reduce the tracking error and increase system robustness. The proposed method is based on Backstepping approach. To reduce the tracking error, resulted from the simplification of the missile model, a nonlinear disturbance observer is used to estimate the uncertainty and also update the reference signal. In addition nonlinear disturbance observer is used to eliminate output disturbance. The advantage of the proposed method is its complete flexibility and also it can be employ for linear and nonlinear systems

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This paper describes load torque estimation (LTE) issue in induction motors with uncertainty, using dynamic sliding mode control (DSMC). In DSMC the chattering is removed due to the integrator which is placed before the input control of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system and then, the plant model should be completely known. To solve this problem, a new nonlinear observer called integral-chain observer (ICO) has been used. The advantage of the proposed approach is to have the system controlled as well as its main task i.e. LTE. Moreover, we assume that only output of system is accessible which is important in practical implementation. Simulation results are presented to demonstrate the approach.

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Obtaining the optimal control low for nonlinear systems is one of the most active subjects in the control theory. Solutions become more complicate in the presence of physical constraints, especially input constrains. In this paper, a new method has been developed to design a constrained nonlinear controller. In this method, the performance index is developed based on the predictive approach. Then, an optimization problem is solved to obtain the optimal control law in the presence of input constraints. Two constrained optimization methods have been used in this paper. The KKT-based method provides an analytical solution for obtaining the control law. The other method is based on the genetic algorithm (GA) which is considered a numerical optimization technique. These methods are implemented on an example and the results are compared

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In this study a model reference rough-radial basis function neural network controller with feedback error learning for control of a class of nonlinear systems subject to unknown bounded uncertainty is proposed. The proposed controller in hybrid form includes the classic controller and rough- radial basis function neural network controller. Because of using the classic controller with the neural network controller, it is expected that the transient response is bounded. The weights of the output layer of the neural network controller are interval variables. Using an appropriate Lyapunov function, stable adaptation laws for these weights according to the output of the classic controller and based on stability are derived. To show the efficacy of the proposed controller, results of simulation that is applied to Duffing Oscillator and Genesio- Tesi are shown and results are compared with the results when simple model reference radial basis function neural network is used as the controller. The results show that the proposed method is more robust against uncertainty when it is compared to model reference radial basis function neural network controller. Also, using the proposed controller, synchronization of chaotic systems is performed. The results verified the effectiveness of the proposed controller.

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The dynamic equations of an autonomous underwater vehicle (AUV) are described as a nonlinear system with multiple hydrodynamic coefficients which strongly affect the performance, maneuverability and controllability of AUV. On the other hand, the values of these coefficients depend on the vehicle speed and the geometric properties. In this paper, the nonlinear model identification problem of NPS AUV II, as a six degree-of-freedom (DOF) autonomous underwater vehicle, is addressed by using the nonlinear continuous-time extended Kalman filter (EKF) observer with guaranteed convergence. To this end, the hydrodynamic coefficients of AUV are considered as the augmented state variables of a six DOF nonlinear model. Based on the input-output data at the presence of the measurement noise of sensors, the state variables and the hydrodynamic coefficients of the nonlinear model in a (path) helical maneuver, are suitably estimated by using the EKF observer. In order to analyze the numerical performance of the proposed method, the dynamic equations of the vehicle are introduced, and a comparison is made between the identified model outputs and those of the real model.

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Abstract:This paper presents an analytical study of the nonlinear effect of squeeze film damping on the previously designed ring shape anchored contour mode RF MEMS disk resonators. Varations of damping coefficient and stiffness constant of the resonator due to squeeze film phenomenon are evaluated and their efects on quality factor of the resonator is calculated. Analytical calculations mention that due to ultra high spring constant of the resonator, resulting frequency pulling and damping related to this effect could be neglected. Based on these derivations, there is no requirement for high price
vacuum packaging for these resonators for increasing their quality factor. Extracting of the behavioral equations for the resonator in the presence of squeeze film effect is crucial in design of resonators in packaging, fabrication process and total cost points of view.

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This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. The proposed control approach is applied to a CSTR. Simulation results show that the proposed robust MPC scheme is quite effective and it has a remarkable performance.

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The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and exploitation characteristics of PSO, this paper introduces computationally fast and efficient IPSO algorithms based on a novel class of exponential learning factors (ELF-PSO). This class contains time-varying exponential learning factors (TELF), random exponential learning factors (RELF), self-adjusting exponential learning factors (SELF) and linear-exponential learning factors (LELF) strategies. Experiment is performed and compared with a set of well-known constant, random, time-varying and adaptive learning factors strategies on a suite of nonlinear benchmark functions. The experimental results and statistical analysis prove that ELF-PSO algorithms are able to solve a wide range of difficult nonlinear optimization problems efficiently. Also these results show that the proposed methods outperform other algorithms in most cases.

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In this paper, a robust model predictive control (MPC) algorithm is designed for nonlinear uncertain systems in presence of the control input constraint. To achieve this goal, first, the additive and polytopic uncertainties are formulated in the nonlinear uncertain system. Then, the control policy is chosen as a state feedback control law in order to minimize a given cost function at each known sample-time. Finally, the robust MPC problem is transformed into another optimization problem subject to some linear matrix inequality (LMI) constraints. The controller gains are determined via the online solution of the proposed minimization problem in real-time. The suggested method is simulated for a second order nonlinear uncertain system. The closed-loop performance is compared to other control techniques. The simulation results show the effectiveness of the proposed algorithm compared to some existing control methods.
 

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 In this paper an observer-based robust fault estimation scheme is proposed for a special class of Lipchitz nonlinear systems where the disturbances and faults are assumed to be coupled with the main system states. In the considered model of system, fault is assumed to enter both of the state and output equations as an unmeasured nonlinear function and coupled with the states. The disturbances and the uncertainties are considered as nonlinear functions coupled with the states. To the best of the authors’ knowledge these conditions have not been previously considered in related papers. In the proposed approach, a Luenberger observer is designed for the estimation of faults and states of system simultaneously. The effect of system disturbances is attenuated with the L2  norm. The necessary conditions for the existence of such observer is expressed in the form of Linear Matrix Inequality. The Lipchitz constant of the nonlinear function is obtained by solving the proposed Linear Matrix Inequality. Finally, the performance of the proposed method is simulated on a three-phase induction motor. The results indicate good performance of the proposed method.
 

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This paper considers the design of observer-based distributed adaptive controllers to achieve a leader-follower consensus for high-order nonlinear multi-agent systems in the presence of non-symmetric input saturation constraint and system uncertainties. Solving the consensus problem for nonlinear multi agent systems in the presence of unknown terms in the dynamics equations of follower that are due to model simplification, parameters uncertainty or external disturbances are investigated. In order to reduce the conservatism, the upper bound of uncertain term is considered to be unknown, which is obtained by adaptive laws. In addition, it is assumed that all states variables of agents are not directly measurable; therefore firstly, by designing the nonlinear distributed observers, the states variables of agents are estimated. Then, by using the sliding mode technique, the observer-based distributed adaptive control laws are designed to ensure the consensus between the agents, and the output of the followers can track the output of the leader, in the presence of non-symmetric input saturation and uncertain terms. Finally, the results of the simulations have completely confirmed the achievements of the proposed laws.

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Owing to an increasing need of control community for providing a precise and integrated model of natural and practical structures switching systems have attracted much attention. On the other hand, the multi-model inherent in many practical systems has increased the importance of reviewing these types of systems. In this paper, the problem of adaptive fault tolerant finite-time control of a class of nonlinear switching systems in the presence of actuator fault, external disturbances and dead-zone input nonlinearity is investigated. The boundary of the uncertain terms of the system is assumed to be unknown and adaptive rules are used to eliminate the destructive effects of these terms on the system response. The subsystems of switching system are considered as nonlinear systems with a canonical structure. This paper sets no restrictive assumption on the switching logic of the system. Therefore, the purpose is to propose a controller that works under any desired switch signal and can overcome the actuator fault, disturbances and dead-zone input nonlinearity. To achieve this purpose, after providing a smooth sliding manifold, an adaptive control input is developed such that the system trajectories approach the prescribed sliding mode dynamics in finite-time sense. Finally, by using the Lyapunov stability theory, it is proved that the origin is the finite-time stable equilibrium point of the overall closed-loop system. The simulation results provided by MATLAB software show the performance of the proposed controller.

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In this paper, the problem of stabilization and synchronization of Lorenz and Chua chaotic in the presence of uncertainty using fractional order sliding mode control strategy based on nonlinear adaptation law has been investigated. Lorenz and Chua systems denote third order dynamics models which are chaotic for certain parameters. The proposed control law is composed of two prats sliding mode control and adaptive control law. Firstly, by supposing that instantaneous information of nonlinear part of chaotic system is not available, a linear regressor equation including an unknown section has been used. Using Lyapunov stability theorem and based on fractional calculus, adaptation law is developed to instantaneous estimation of unknown part. Moreover, by defining based on error signals and realizing exponential reaching law for insuring closed-loop stability, the sliding mode control law including equivalent and switching control has been derived. Eventually, the final control law has been derived by synthesizing sliding mode control and adaptive laws. The important aspect of the proposed approach is ability to encounter unstructured uncertainties and nonlinear effects of chaotic systems dynamic and guiding the state variables into sliding surface for arbitrary initial conditions. The performance of the proposed algorithm has been evaluated by realizing the stabilization problem of chaotic Lorenz system and synchronization of chaotic Lorenz and Chua systems.
 

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Electromagnetic suspension technology has been developed in recent years due to advantages such as no contact and reduced friction. Of course, ensuring efficiency in these systems requires precise control of the position of the suspended object. Therefore, electromagnetic suspension is considered as a process by control engineers. The dynamics of electromagnetic suspension systems is nonlinear and also include model and parametric uncertainties such as the weight of the suspended object. In this paper, a finite time nonlinear hybrid method is used to stabilize the electromagnetic suspension system. Proof of finite time stability of the proposed method is performed using Lyapunov theory and a relation for calculating the convergence time depends on the controller gains is presented. To ensure the finite time convergence of the system state and output variables, the backstepping algorithm is used and in each step, the finite-time convergence theory is used. The controller designed in this paper is compared with the backsteping method and the superiority of the proposed method in various simulations is shown.

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Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.