Control
hadi chahkandi nejad; Mohsen Farshad; Ramazan Havangi
Abstract
In this study, an adaptive controller for LTI systems with unknown and time varying input time delay is presented with the purpose of tracking. Due to the large area considered for time delay variations, the structure of the proposed controller is considered to be in form of Multiple Model Adaptive Control ...
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In this study, an adaptive controller for LTI systems with unknown and time varying input time delay is presented with the purpose of tracking. Due to the large area considered for time delay variations, the structure of the proposed controller is considered to be in form of Multiple Model Adaptive Control (MMAC). The presented adaptive control system is of indirect type, i.e., at any moment of time, first, one band of time delay is identified using a proposed estimator, and then with a switching rule in the supervisory subdivision, the main control signal, which is a linear combination of multiple controllers output, forms. In fact, each of the multiple controllers in MMAC structure with optimal weights, participate in forming the main control signal. The multiple controllers used in this study are of PID type. It should be noted that the parameters for each of the multiple controllers, for the system under control, are adjusted offline and proportional to its corresponding time-delay sub-band using the genetic algorithm. Finally, simulation results show the relatively desirable performance of the proposed control system and observer in facing with large unknown and time varying delays.
Control
javad keighobadi; Mohammad mehdi Fateh
Abstract
Recent research on the backstepping control of robotic systems has motivated us to design a robust backstepping voltage-based controller with computational simplicity and ease of implementation. In this paper, an adaptive robust tracking controller based on backstepping method (ARTB) is presented for ...
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Recent research on the backstepping control of robotic systems has motivated us to design a robust backstepping voltage-based controller with computational simplicity and ease of implementation. In this paper, an adaptive robust tracking controller based on backstepping method (ARTB) is presented for uncertain electrically-driven robotic manipulators in the framework of voltage control strategy. It is intended to convert robot control problem to motor control problem. In the design procedure, the manipulator dynamics are incorporated into a lumped uncertainty, such that the proposed adaptation law promptly compensates for it. Hence, high tracking accuracy, robust behavior and less complexity are the prominent features of the proposed control system in the presence of external disturbances, parametric uncertainties and un-modeled dynamics. Moreover, the control approach is useful for high-speed tracking purposes. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and the tracking error converges to zero asymptotically. As a case study, the proposed ARTB is simulated on a two-link robot manipulator driven by permanent magnet DC motors. Numerical simulations are included to show the superiority of the proposed controller to a state augmented adaptive backstepping method, a sliding backstepping controller and an adaptive backstepping sliding mode control in tracking the desired trajectory.
Control
S.Raheleh Shahrokhi; Hamid Khaloozadeh; HamidReza Momeni
Abstract
Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the ...
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Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the drawcard of any investor in competition with others. Therefore, having a predictive approach with the lowest degree of error will lead to smarter management of resources. Due to the complex and stochastic nature of the stock market, conventional forecasting approaches in this field have usually faced serious challenges, most notably losing the robustness when the data type changed over time. Moreover, by focusing on point forecasting, some useful statistical information about the objective random variable has been ignored inadvertently, undermining the prediction efficiency. The focus of this study is on density forecasting models which, unlike point forecasting, contain a description of uncertainty. Also, to take advantage of the diversity and robustness features of the combination, instead of an individual prediction, a combination of the density forecasting given by the different structures of ARMA, ANN, and RBF models is presented. In order to analyze the capabilities of these approaches in Tehran Stock Exchange (TSE), two basic methods of this category have been used to predict the price of MAPNA stock -one of the fifty active companies in this market- in the period 2012 to 2019.
Control
Hojjat Hajiabadi; Mohsen Farshad; MohammadAli Shamsinejad
Abstract
Fossil fuel combustion in power plants is the world’s most significant threat to people’s health and the environment. Recently, wind power, as a clean, sustainable and renewable source of energy, has attracted many researchers. The present paper studies how to maximize the extraction of wind ...
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Fossil fuel combustion in power plants is the world’s most significant threat to people’s health and the environment. Recently, wind power, as a clean, sustainable and renewable source of energy, has attracted many researchers. The present paper studies how to maximize the extraction of wind power and the efficiency of a switched reluctance generator (SRG) by firing angles control. The proposed scenario comprises the optimization of turn-on and turn-off angles in the offline mode using a particle swarm optimization algorithm to control the system in the online mode with linear interpolation. The present approach simultaneously investigates the firing angles; also, it has simple structure, low execution time, and efficient convergence rate that are independent of machine characteristics (regardless of high nonlinearity of SRG). Furthermore, copper losses, as well as switching and conduction losses of semiconductors, were considered in simulations to achieve a more realistic outcome. Ultimately, the simulation results of a typical three-phase 6/4 generator using Matlab confirmed the validity of the presented control strategy that can easily find applications in the future.
Control
Ahmad Mirzaei; amin ramezani
Abstract
In this paper two linear constrained cooperative distributed extended dynamic matrix control (CDEDMC) and adaptive generalized predictive control (CDGPC) are proposed to control the uncertain nonlinear large-scale systems. In these approaches, a proposed cooperative optimization is employed which improves ...
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In this paper two linear constrained cooperative distributed extended dynamic matrix control (CDEDMC) and adaptive generalized predictive control (CDGPC) are proposed to control the uncertain nonlinear large-scale systems. In these approaches, a proposed cooperative optimization is employed which improves the global cost function. The cost values and convergence time are reduced using the proposed cooperative optimization strategy. The proposed approaches are designed based on the compensation of the mismatch between linearized and nominal nonlinear models. In CDEDMC the mismatch is considered as a disturbance and compensated; Also in CDGPC it is compensated using online identification of the linearized model. The typical distributed linear algorithms like DMC leads to an unstable response if the reference trajectory is a little far from the equilibrium point. This problem will be partially solved using the CDEDMC and will be completely solved using the CDGPC even if the reference trajectory is too far from the equilibrium point. The performance and effectiveness of proposed approaches are demonstrated through simulation of a typical uncertain nonlinear large-scale system.
Control
Maryam Alipour; Pooneh Omidiniya
Abstract
This paper, proposes an approximate analytical method to solvea class of optimal control problems. This method is an enhancementof the variational iteration method (VIM) which is called modifi ed variational iteration method (MVIM) and eliminates all additional calculations in VIM, thus requires less ...
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This paper, proposes an approximate analytical method to solvea class of optimal control problems. This method is an enhancementof the variational iteration method (VIM) which is called modifi ed variational iteration method (MVIM) and eliminates all additional calculations in VIM, thus requires less time to do the calculations. In thisapproach, first, the optimal control problem is converted into a non-linear two-point boundary value problem via the Pontryagins maximum principle, and then we applied the MVIM method to solve this boundary value problem. This suggested method is suitable for a large class of non-linear optimal control problems that for the non-linear part of the problem, we used the Taylor series expansion. In the end, three examples are provided to demonstrate the simplicity and efficiency of the method. The numerical results of the proposed method versus other methods are presented in tables. All calculations were carried out using Mathematica software.
Control
Mohammad Reza Shokoohinia; Mohammad Mehdi Fateh
Abstract
In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the ...
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In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the applied torques to the robot joints, motor voltages are computed by the control law. First, a dynamic sliding mode control is designed for the robotic system. Then, to enhance the tracking performance of the system, an adaptive mechanism is developed and integrated with the dynamic sliding mode control. Since the lumped uncertainty is unknown in practical applications, the uncertainty upper bound is necessary in the design of the dynamic sliding mode controller. Hence, the lumped uncertainty is estimated by an adaptive law. The stability of the closed-loop system is proved based on the Lyapunov stability theorem. The simulation results demonstrate the superior performance of the proposed adaptive dynamic sliding mode control strategy.
Control
Allahverdi Azadrou; Siamak Masoudi; Reza Ghanizadeh; Payam Alemi
Abstract
This work deals with minimizing fluctuations of propulsion force and improving the motion quality in a linear switched reluctance motor. In order to minimize the jerks in the moving part of the motor, a new profile has been used to generate an appropriate reference speed profile. The results indicate ...
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This work deals with minimizing fluctuations of propulsion force and improving the motion quality in a linear switched reluctance motor. In order to minimize the jerks in the moving part of the motor, a new profile has been used to generate an appropriate reference speed profile. The results indicate that at speed 0.5 m/s, the motor reaches its command speed at the proposed time while, using conventional speed profile it takes almost 1.4 times the desired time. In order to control the speed and incease the motion quality, a simple fuzzy logic system has been used which is able to overcome the uncertainties problem in nonlinear systems. The fuzzy control system can regulate the motor performance so that it tracks the reference speed with minimum error and fluctuation. To illustrate the performance of the fuzzy method, a conventional PI method along with a model reference adaptive control (MRAC) strategy have been applied to the motor and the obtained results for three control methods have been compared. Speed overshoot using conventional PI method is about 20 percent of the final speed while this is about 6 percent for fuzzy and MRAC methods. The system is designed and its efficiency is shown through simulation and experimental tests in different performance situations . The obtained results confirm that the fuzzy strategy outperforms other methods.
Control
Salehe Afsharian; Hussein Eliasi
Abstract
This article aims to derive new sufficient conditions to guarantee the stability of piecewise affine systems with time-varying delay (PWA-TVD). The set of delay-dependent linear matrix inequality (LMI) describes the novel stability criteria. This approach considers the PWA-TVD system with a time-delayed ...
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This article aims to derive new sufficient conditions to guarantee the stability of piecewise affine systems with time-varying delay (PWA-TVD). The set of delay-dependent linear matrix inequality (LMI) describes the novel stability criteria. This approach considers the PWA-TVD system with a time-delayed state-dependent switching signal. The newly suggested Lyapunov-Krasovskii functional (L-K-F) and improved estimation of its derivative have a crucial role in decreasing the complexity and conservatism of the proposed stability results. The suggested L-K-F belongs to the current and time-delayed states, the integral of the states over the time-varying delay, and time derivation of the states. A new inequality was used to obtain an upper bound (UB) for the time derivation of the Lyapunov functional. Then based on this UB, less conservative results are achieved. The theoretical results are applied to the numerical examples. The results confirm the effectiveness of the presented method. The conservative index is the maximum admissible UB of time delay.
Control
Mohsen Jalaeian-F; Mohammad Mehdi Fateh; Morteza Rahimiyan
Abstract
This paper presents a novel optimal impedance voltage-controller for Electrically Driven Lower Limb Rehabilitation Robots (EDLR). To overcome the dynamical complexities, and handle the uncertainties, the proposed method employs an expected forward model of the actuator. The difference between this model’s ...
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This paper presents a novel optimal impedance voltage-controller for Electrically Driven Lower Limb Rehabilitation Robots (EDLR). To overcome the dynamical complexities, and handle the uncertainties, the proposed method employs an expected forward model of the actuator. The difference between this model’s output and the actual output represents the existing value of lamped uncertainty. A voltage-controller is designed based on this uncertainty estimator, which compensate for the uncertainties. Parameters of the controller have been optimized using genetic algorithms. Key contributions of this paper are I) estimation of the uncertainty by the expected model’s output, II) overcoming the changes in motor parameters, III) introducing a class of closed-loop system termed as “Repeatable”, and IV) designing an optimal impedance voltage-controller that is non-sensitive to the parameter variations. Significant merits of the approach are swift calculations, efficiency, robustness, and guaranteed stability. Furthermore, the simplicity of design, ease of implementation and model-free independent joint structure of the approach are noticeable. The method is compared with an adaptive robust sub-controller and a Taylor-series-based adaptive robust controller, through simulations in passive range of motion and active assistive rehabilitation exercises. The results show the superiority of the proposed method in tracking performance and the time of calculations.
Control
Shoorangiz Shams Shamsabad Farahani; Narges Masoomabadi; Mohammad Reza Jahed-Motlagh
Abstract
Based on the recent Internet advances, congestion control is considered as an important issue and has spurred a significant amount of research. In this study, second-order sliding mode control is used to adjust the average queue length and maintain the closed-loop system performance. The control law ...
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Based on the recent Internet advances, congestion control is considered as an important issue and has spurred a significant amount of research. In this study, second-order sliding mode control is used to adjust the average queue length and maintain the closed-loop system performance. The control law is obtained in two steps. First, the nonlinear state-space form of the network is extracted based on state variables as the average queue length and congestion window size. Then, the proportional-Integrator-derivative and proportional- derivative sliding surface are defined according to the tracking error. Also, in order to avoid chattering, the derivative of the sliding surface is considered and the closed-loop system stability is investigated based on Lyapunov theory. The proposed scheme renders good tracking specifications and closed-loop system robustness. The simulation results show that the proposed methods outperform proportional integral (PI) and proportional integral derivative (PID) schemes. Also, robustness to disturbances increases and chattering and transient response degradation are avoided.
Control
Saeed Rahmati; Hussein Eliasi
Abstract
This paper presents a robust decentralized model predictive control scheme for a class of discrete-time interconnected systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive time-varying perturbation function which presents the interconnections ...
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This paper presents a robust decentralized model predictive control scheme for a class of discrete-time interconnected systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive time-varying perturbation function which presents the interconnections and is generally uncertain and nonlinear, but it satisfies a quadratic bound. Using the dual-mode MPC stability theory and Lyapunov theory for discrete-time systems, a sufficient condition is constructed for synthesizing the decentralized MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. To guarantee robust asymptotic stability, sufficient conditions for designing MPC stabilizing components are characterized in the form of an LMI optimization problem. The proposed control approach is applied to a system composed of five coupled inverted pendulums, which is a typical interconnected system, in a decentralized fashion. Simulation results show that the proposed robust MPC scheme is quite effective and has a remarkable performance.
Control
Azar Shabani; Alireza Fatehi; Fahimeh Soltanian; Reza Jamilnia
Abstract
In this paper, two semi-analytical techniques are introduced to compute the solutions of differential-algebraic equations (DAEs), called the Least Squares Repetitive Homotopy Perturbation Method (LSRHPM) and the Least Squares Span Repetitive Homotopy Perturbation Method (LSSRHPM). The truncated series ...
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In this paper, two semi-analytical techniques are introduced to compute the solutions of differential-algebraic equations (DAEs), called the Least Squares Repetitive Homotopy Perturbation Method (LSRHPM) and the Least Squares Span Repetitive Homotopy Perturbation Method (LSSRHPM). The truncated series solution by the homotopy perturbation method only is suitable for small-time intervals. Therefore, to extend it for long time intervals, we consider the Repetitive Homotopy Perturbation Method (RHPM). To improve the accuracy of the solutions obtained by RHPM and to reduce the residual errors, least squares methods and span set are combined with RHPM. The proposed methods are applied to solve nonlinear differential-algebraic equations and optimal control problems. The results of the proposed methods are compared using some illustrative examples. The obtained results demonstrate the effectiveness and high accuracy of the new modifications. The effect of the parameters on the accuracy and performance of the methods are studied through some illustrative examples
Control
Amin Noori; Mohammad Ali Sadrnia; Mohammad Bagher Naghibi-Sistani
Abstract
In this paper, the main focus is on blood glucose level control and the possible sensor and actuator faults which can be observed in a given system. To this aim, the eligibility traces algorithm (a Reinforcement Learning method) and its combination with sliding mode controllers is used to determine the ...
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In this paper, the main focus is on blood glucose level control and the possible sensor and actuator faults which can be observed in a given system. To this aim, the eligibility traces algorithm (a Reinforcement Learning method) and its combination with sliding mode controllers is used to determine the injection dosage. Through this method, the optimal dosage will be determined to be injected to the patient in order to decrease the side effects of the drug. To detect the fault in the system, residual calculation techniques are utilized. To calculate the residual, it is required to predict states of the normal system at each time step, for which, the Radial Basis Function neural network is used. The proposed method is compared with another reinforcement learning method (Actor-Critic method) with its combination with the sliding mode controller. Finally, both RL-based methods are compared with a combinatory method, Neural network and sliding mode control. Simulation results have revealed that the eligibility traces algorithm and actor-critic method can control the blood glucose concentration and the desired value can be reached, in the presence of the fault. However, in addition to the reduced injected dosage, the eligibility traces algorithm can provide lower variations about the desired value. The reduced injected dosage will result in the mitigated side effects, which will have considerable advantages for diabetic patients.
Control
Mostafa Rahideh; Abbas Ketabi; Abolfazl Halvaei Niasar
Abstract
In this paper, an adaptive control method is proposed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. For improving the performance of an MPPT, this study develops a two-level adaptive control structure that can decrease difficulty in system control and efficiently handle the uncertainties ...
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In this paper, an adaptive control method is proposed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. For improving the performance of an MPPT, this study develops a two-level adaptive control structure that can decrease difficulty in system control and efficiently handle the uncertainties and perturbations in the PV systems and the environment. The first control level is a ripple correlation control (RCC), and the second level is a model reference adaptive control (MRAC). This paper emphasizes mainly on designing the MRAC algorithm, which improves the underdamped dynamic response of the PV system. The original state-space equation of PV system is time-varying and nonlinear, and its step response contains oscillatory transients that damp slowly. Using the extended state-dependent Riccati equation (ESDRE) approach, an optimal law of the controller is derived for the MRAC system to remove the underdamped modes in PV systems. A algorithm of scanning the P-V curve of the PV array is proposed to seek the global maximum power point (GMPP) in the partial shading conditions (PSCs). It is shown that the proposed control algorithm enables the system to converge to the maximum power point in milliseconds in partial shading conditions .
Control
fahimeh akhavan ghassabzadeh; Samaneh Soradi zeid
Abstract
Due to the easy adaption of radial basis functions (RBFs), a directRBF collocation method is considered to develop an approximate scheme to solvefractional delay differential equations (FDDEs). The method of RBFs is a method of scattered data interpolation that has many application in different fields. ...
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Due to the easy adaption of radial basis functions (RBFs), a directRBF collocation method is considered to develop an approximate scheme to solvefractional delay differential equations (FDDEs). The method of RBFs is a method of scattered data interpolation that has many application in different fields. In spite of easy implementation of other high-order methods and finite difference schemes for solving a problem of fractional order derivatives, the challenge of these methods is their limited accuracy, locality, complexity and high cost of computing in discretization of the fractional terms, which suggest that global scheme such as RBFs that are more accurate way for discretizing fractional calculus and would allow us to remove the ill-conditioning of the system of discrete equations. Applications to a variety ofproblems confirm that the proposed method is slightly more efficient than thoseintroduced in other literature and the convergence rate of our approach is high.
Control
Amin Karimi; YousefReza Jafarian; Hassan Bevrani; Rahmatollah Mirzaei
Abstract
The use of renewable energy sources in microgrids has grown dramatically in recent years. The absence of a rotational mass in these microgrids and their interfaces leads to a lack of inertia and consequently, frequency and voltage instability. To cope with these dilemmas, the virtual synchronous generator ...
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The use of renewable energy sources in microgrids has grown dramatically in recent years. The absence of a rotational mass in these microgrids and their interfaces leads to a lack of inertia and consequently, frequency and voltage instability. To cope with these dilemmas, the virtual synchronous generator (VSG) has been introduced as an effective solution. This paper first focuses on modeling a VSG using basic electrical equations. It, then, proffers a transient fuzzy controller augmented on virtual inertia’s topology. Inspired by the FACTS’ performance, the privileged specifications such as STATCOM fluctuation damping ability for major perturbations at transient times are appended to the VSG scheme by a fuzzy controller. This controller is implemented with a feedback from the system voltage angle and its derivative, as well as in frequency and its derivative. The modified coefficients of both active and reactive powers are outputs of the fuzzy system. Using the proposed fuzzy controller, the transient response of VSG-based microgrids is improved. Simplicity and ability to improve the transient response are the principal specifications of the proposed configuration. Simulation results confirm the improvement of the presented method by the introduced augmented VSG control mechanism.
Control
Farnaz Sabahi
Abstract
Abstract— One of the main problems underlying most optimization theories is local optimum. When time delays are presented, this issue becomes much more problematic. In such conditions, evolutionary optimization algorithms are proven to be helpful. In this paper, quantum genetic algorithm (QGA) ...
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Abstract— One of the main problems underlying most optimization theories is local optimum. When time delays are presented, this issue becomes much more problematic. In such conditions, evolutionary optimization algorithms are proven to be helpful. In this paper, quantum genetic algorithm (QGA) has been used to tackle the stated problem in the framework of delay-dependent linear matrix inequality (LMI) robust H∞ control. QGA is employed to find suitable feedback gains and delay-dependent LMI solvers are concerned to resolve stability issues. In addition, to provide more balance between exploration and exploitation, to increase convergence rate as well as to prevent premature convergence, it is proposed that particle swarm optimization (PSO) is augmented with QGA. Simulation is dealt with LMI-based H∞ control scheme of the QGA and QGA-PSO optimization space from the design point of one-degree freedom single link scara robot. The whole controller satisfies the desired properties for uncertain-but-known constant bounded time delay. Furthermore, one of the drawbacks found in tests of most hybrid global-local strategies, i.e. premature convergence, has been cancelled by the proposed scheme of QGA and PSO.
Control
Sobhi Baniardalani
Abstract
This paper deals with fault diagnosis of a linear continuous variable dynamical system represented by a discrete state space model. The proposed fault diagnoser is based on a special Petri Net called Continuous Time Delay Petri Net (CTDPN). Thanks to the theorem presented in this paper, an exact correspondence ...
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This paper deals with fault diagnosis of a linear continuous variable dynamical system represented by a discrete state space model. The proposed fault diagnoser is based on a special Petri Net called Continuous Time Delay Petri Net (CTDPN). Thanks to the theorem presented in this paper, an exact correspondence between discrete-state space equations and fundamental equations of the CTDPN can be established. Based on this theorem, a systematic method is presented for realization of classical parity equations by a CTDPN that plays the role of the fault diagnoser. By integrating the concept of state space models and Petri Nets in this paper, new and effective methods can be proposed for analyzing and fault diagnosis of hybrid systems. Finally, the performance of the proposed method is investigated for fault diagnosis of a DC motor. The results show that with the help of proposed Petri net, fault diagnosis can be done well and traditional diagnoser can be replaced with this network.
Control
farnaz sabahi
Abstract
Abstract—In this article, a new hybrid feedback system is introduced, which integrates the behavior- based planning by reactive agent-based control scheme through subsumption architecture. At first, subsumption protocol studies the interactions of robot with its environment which cover problems ...
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Abstract—In this article, a new hybrid feedback system is introduced, which integrates the behavior- based planning by reactive agent-based control scheme through subsumption architecture. At first, subsumption protocol studies the interactions of robot with its environment which cover problems including translating of agent action into an outcome, interactions with the environment, and cooperative actions. Second considers deliberative behavior given the prevailing protocol. It determines the best and quickest response for each agent and tunes the actions based on an objective function obtained by a leader agent. More specifically, tasks are arranged as a hierarchy, where the high-level task is obstacle avoidance. Conflicting lower level tasks are removed by the leader agent decisions. Indeed, the leader agent can adjust the priority of all action to provide an optimal behavior. In other words, our new agent-based method optimizes the subsumption architecture by producing an approximating objective function that made not only behaviors but also optimization done in incremental procedure. We also define an emergency avoidance factor that made higher speed still stable and better interaction of robot in the presence of obstacles. For obstacles avoidance, the leader agent projects a plane to investigate the space ahead and continues. Finally, the leader agent makes a basic stand by task sharing behaviors in decentralized manner using subsumption architecture to draw an optimal path. Simulation results show that although the proposed apporach has little knowledge about the unexpected and adhoc situation in the robot’s environment, it is able to provide suitable performance.
Control
Ali Karsaz; Amin Adineh
Abstract
Abstract: A hybrid unknown input estimation based on a new two-sample backward model and data fusion for high maneuvering target tracking is proposed. This new approach is based on the consideration of more than one state and input components from the current single observation. These extracted state ...
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Abstract: A hybrid unknown input estimation based on a new two-sample backward model and data fusion for high maneuvering target tracking is proposed. This new approach is based on the consideration of more than one state and input components from the current single observation. These extracted state and input components would be augmented in a single vector, and the final estimation for unknown target acceleration will be determined. Using a combination of the new backward modeling and traditional modified input estimation (MIE) technique, more information will be extracted. This new hybrid scheme which using more input information can better estimate the target maneuvering structure. Despite the traditional methods, the proposed algorithm introduces two different strategies to state the input estimation including online and delayed estimation scenarios. Also, this paper suggests several different data fusion methods through these strategies. The results are compared with a typical MIE method to evaluate the performance of the proposed hybrid scheme especially for problems in high maneuvering target tracking. The results show that the backward algorithm makes advantages such as reduction of the transient state error and more stability for the estimation by an appropriate combination of the MIE estimator.
Control
Seyed-mohamad-emad Oliaee; Mohamad Teshnehlab; Mehdi Aliyari-shore-deli
Abstract
The Local Model Network (LMN) is one of the common structures to model systems and fault detection and identification. This structure covers the disadvantages of training in fuzzy systems and interpretations in neural networks at the same time. But the algorithms that have been introduced to create LMN, ...
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The Local Model Network (LMN) is one of the common structures to model systems and fault detection and identification. This structure covers the disadvantages of training in fuzzy systems and interpretations in neural networks at the same time. But the algorithms that have been introduced to create LMN, such as LOLIMOT, are very sensitive to the dimension of input space. In other words, the search space and the number of network parameters are increased exponentially by increasing the input dimension, which is called the curse of dimensionality. Therefore in this paper, the LMN structure has been developed, and a new incremental algorithm has been proposed which is based on Genetic algorithm and LOLIMOT algorithm that is called GLOLIMOT. The proposed idea reduces the search space dimension and also optimizes it. The proposed idea and the traditional structure are tested on single-shaft industrial gas turbine prototype model, which has high complexity and high dimension. The results indicate improvement in performance of the proposed structure and algorithm.
Control
Sara Haghighatnia; Heydar Toossian Shandiz; Alireza Alfi
Abstract
In this paper, a novel conformable fractional order (FO) sliding mode control technique is studied for a class of FO chaotic systems in the presence of uncertainties and disturbances. First, a novel FO nonlinear surface based on conformable FO calculus is proposed to design the FO sliding mode controller. ...
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In this paper, a novel conformable fractional order (FO) sliding mode control technique is studied for a class of FO chaotic systems in the presence of uncertainties and disturbances. First, a novel FO nonlinear surface based on conformable FO calculus is proposed to design the FO sliding mode controller. Then, asymptotic stability of the controller is derived by means of the Lyapunov direct method via conformable FO operators. The stability analysis is performed in the sliding and reaching phase. In addition, the realization of reaching phase is guaranteed in finite time and the reaching time is calculated analytically. The proposed control approach has some superiorities. Reduction of the chattering phenomenon, high robustness against the uncertainty and external disturbance, and fast convergence speed are the main advantages of the proposed control scheme. Moreover, it has simple calculations because of using conformable FO operators in the control design. The numerical simulations verify the efficiency of the proposed controller.
Control
Valiollah Ghaffari
Abstract
In this paper, an open loop control scheme is developed in order to design a dead-beat control effort in the high order continuous-time systems. The dead-beat control is really a finite-time control law. In this method, the input signal has been manually selected such that the output signal becomes constant ...
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In this paper, an open loop control scheme is developed in order to design a dead-beat control effort in the high order continuous-time systems. The dead-beat control is really a finite-time control law. In this method, the input signal has been manually selected such that the output signal becomes constant in a finite time. In the LTI systems, having known the step response, a control signal could be exactly selected such a way that the control objective would be met in a finite time. For this end, the dead-beat control problem is firstly investigated in a standard first order system. Then a similar problem is studied in the second order systems. Finally a general design framework would be developed to obtain a dead-beat control policy in the high order continuous-time systems. In the proposed method, the control design problem is deliberately converted into the solution of a linear matrix equation. Therefore the control signal would be determined by solving such an algebraic equation. The proposed procedure is applied in some continuous-time LTI systems. The simulation results are shown effectiveness of the suggested methods for designing of a finite-time control law in the continuous-time systems.
Control
YousefReza Jafarian; Amin Karimi; Hassan Bevrani
Abstract
Compared to individual DC or AC microgrids, the Hybrid microgrids (HMGs) are more efficient and inexpensive due to eliminating of multiple DC-AC-DC conversions. In HMGs, where AC loads are supplied by DC link, load demand disturbance has direct negative effects on the DC link voltage. In this study, ...
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Compared to individual DC or AC microgrids, the Hybrid microgrids (HMGs) are more efficient and inexpensive due to eliminating of multiple DC-AC-DC conversions. In HMGs, where AC loads are supplied by DC link, load demand disturbance has direct negative effects on the DC link voltage. In this study, primary and secondary controllers are applied to realize suitable operation conditions and control the microgrid converters. Each converter has primary controller to compensate the demand power fluctuations. The secondary controller is also designed for extra demand varieties and sends the proper control signals for primary controllers. The expressed capability of primary controllers can be obtained by designing a simple and robust secondary controller. Hence, the effects of demand fluctuations are eliminated and the system is stabilized. The overall state space model of system is conducted for stability analysis. To demonstrate the proposed controller efficiency, a prototype HMG is modeled and simulated. The stability analysis reveals that the system is stable when the secondary controller tracks the error signal of DC link. Simulation results show that the proposed method could efficiently manage the AC side voltage under load fluctuations.
