Control
Amir Abolmasoumi; Seyed Mohamad Ali Beladi Pour; Mahdi Soleymani; Mazdak Ebadi
Abstract
Electrical energy regeneration and storage in a tall structure with the installed passive pendulum tuned mass and damper (PPTMD) is investigated. While the passive vibration absorbing system works as an energy harvesting device, an electrical system including an electric motor, power electronic converters, ...
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Electrical energy regeneration and storage in a tall structure with the installed passive pendulum tuned mass and damper (PPTMD) is investigated. While the passive vibration absorbing system works as an energy harvesting device, an electrical system including an electric motor, power electronic converters, a battery charger and storage subsystem are designed in order to store the energy taken from the structure vibrations which may be resulted from various external disturbances such as wind or earthquakes. The whole 76-story structure and the relevant electrical energy regeneration system are modeled and simulated and the design scheme is implemented on a two-story reduced order lab structure equipped with PPTMD, the electronic circuit and the battery. A boost AC rectifier is designed and controlled to rectify the AC output voltage and is followed by a boost DC-DC converter as a battery charger for the Li-ion battery. A passivity-based controller (PC) and a sliding mode controller are designed for the rectifier and the battery charger, respectively. The simulation and the real test results demonstrate the efficient harvesting and storage of the energy extracted from the building.
Control
Amir Razzaghian; Reihaneh Kardehi Moghaddam; Naser Pariz
Abstract
The paper introduces a novel adaptive fuzzy fractional-order (FO) fast terminal sliding mode control procedure for a class of nonlinear systems in the presence of uncertainties and external disturbances. For this purpose, firstly, using fractional calculus, a new FO nonlinear sliding surface is proposed ...
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The paper introduces a novel adaptive fuzzy fractional-order (FO) fast terminal sliding mode control procedure for a class of nonlinear systems in the presence of uncertainties and external disturbances. For this purpose, firstly, using fractional calculus, a new FO nonlinear sliding surface is proposed and then, the corresponding FO fast terminal sliding mode controller (FOFTSMC) is designed to satisfy the sliding condition in finite time. Next, to eliminate the chattering phenomenon, a fuzzy system is constructed to design a continuous switching control law. The finite-time stability of the proposed adaptive fuzzy FOFTSMC (AFFOFTSMC) is proved using the concept of Lyapunov stability theorem. Finally, to illustrate the effectiveness of the proposed AFFOFTSMC, three examples are given as case studies. The numerical simulation results confirm the superiority of the proposed controller, which are the better robust performance, faster convergence, finite-time stability of the closed-loop control system, and a chattering free control effort compared to other mentioned control methods.
Control
Razieh Heidari; Alimorad Khajehzadeh; Mahdiyeh Eslami
Abstract
In this paper, an adaptive event-triggered consensus problem considering the time delay of the communication network is studied for heterogeneous multi-agent systems. An event-triggered interval is here considered as a specific delay and unified round trip time (RTT) delay. Furthermore, an efficient ...
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In this paper, an adaptive event-triggered consensus problem considering the time delay of the communication network is studied for heterogeneous multi-agent systems. An event-triggered interval is here considered as a specific delay and unified round trip time (RTT) delay. Furthermore, an efficient optimal predictive-based coordination control strategy is introduced for balancing the non-ideal behaviors of communication channels. In order to evaluate the efficiency of the proposed method for controlling network-based multi-agent systems with coupled subsystems, two stages are studied. In the first stage, the very method is implemented on two coupled continuous stirred tank reactors while in the second one, it is used for controlling the voltage and current of a DC microgrid consisting of several distributed generation units. To prevent the unessential utilization of communication resources, the transfer of information will actually occur in this mechanism if a specific event is triggered. The simulation results show the fact that in spite of being non-ideal and time-delayed communication channels, the proposed technique is capable for improving the performance of power grids.
Control
Emad Hadian; Hamidreza Akbari; Mehdi Farzinfar; Seyed Amin Saeed
Abstract
Management and control of charging/discharging of Electric vehicles (EVs) with the aim of profitability for the Distribution System Operator (DSO) and the private sector is one of the challenges in operating Electric vehicles Charging Stations (EVCS). This paper proposes a novel methodology for optimal ...
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Management and control of charging/discharging of Electric vehicles (EVs) with the aim of profitability for the Distribution System Operator (DSO) and the private sector is one of the challenges in operating Electric vehicles Charging Stations (EVCS). This paper proposes a novel methodology for optimal planning of charging/discharging of the hybrid wind- EVCS which on the one hand, lead to correction of the load curve and on the other hand, improves the grid resilience in extreme weather conditions. In the proposed methodology, since the weather-based outages lead to consumer interruptions, the idea of profit-sharing between DSO and EVCS owners is proposed to incentivize the owner to implement the obtained charging/discharging schedule. To this end, firstly, a Monte-Carlo based stochastic framework for forecasting the probability of weather-based line outages and also modelling uncertainties is devised. Then, a resilience-oriented multi-objective optimization algorithm is presented that, while coordinating the operation of the wind turbine, EV management and Demand Response Programs (DRP), the profits of both EVCS and DSO are maximized during daily operation planning. The resiliency improvement of the proposed method is evaluated by using metrics. The obtained optimal results prove the effectiveness of the proposed method in increasing resiliency and benefits for all players.
Control
Omid Moradi; Saeed Abazari; Nima Mahdian
Abstract
This paper proposes a nonlinear model by an optimal stabilizing controller for weak/islanded grids using a unified power quality conditioner (UPQC). The UPQC can be employed to stabilize a grid-tie inverter (GTI) or a synchronous generator (SG) with minimum control effort. The idea that GTI behavior ...
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This paper proposes a nonlinear model by an optimal stabilizing controller for weak/islanded grids using a unified power quality conditioner (UPQC). The UPQC can be employed to stabilize a grid-tie inverter (GTI) or a synchronous generator (SG) with minimum control effort. The idea that GTI behavior is like the synchronous generator is implemented in this study. The research aims at using an advanced controller to reduce oscillations and achieve stability in a micro-grid. Here, the robust sliding mode controller-based UPFC is employed to design an optimal grid stabilizer. The paper considers variations in UPQC terminal voltage during the transient period of the system, unlike other articles that assume it to be constant. The performance of the proposed algorithm is evaluated by two benchmark networks. The paper presents a comparative study of transient stability in a micro-grid system under different loads. Simulation results reflect the robustness of the proposed sliding mode controller for oscillation reduction in comparison with the Lyapunov-based nonlinear optimal controller and PI controller. In addition, results show the effectiveness of the proposed nonlinear controller in controlling both GTI and SG in the micro-grid system.
Control
Majid Akbarian; Naser Pariz
Abstract
Lyapunov's theorem is the basic criteria to establish the stability properties of the nonlinear dynamical systems. In this method, it is a necessity to find the positive definite functions with negative definite or negative semi-definite derivative. These functions that named Lyapunov functions, form ...
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Lyapunov's theorem is the basic criteria to establish the stability properties of the nonlinear dynamical systems. In this method, it is a necessity to find the positive definite functions with negative definite or negative semi-definite derivative. These functions that named Lyapunov functions, form the core of this criterion. The existence of the Lyapunov functions for asymptotically stable equilibrium points is guaranteed by converse Lyapunov theorems. On the other hand, for the cases where the equilibrium point is stable in the sense of Lyapunov, converse Lyapunov theorems only ensure non-smooth Lyapunov functions. In this paper, it is proved that there exist some autonomous nonlinear systems with stable equilibrium points that despite stability don’t admit convex Lyapunov functions. In addition, it is also shown that there exist some nonlinear systems that despite the fact that they are stable at the origin, but do not admit smooth Lyapunov functions in the form of V(x) or V(t,x) even locally. Finally, a class of non-autonomous dynamical systems with uniform stable equilibrium points, is introduced. It is also proven that this class do not admit any continuous Lyapunov functions in the form of V(x) to establish stability.
Control
Aylar Khooshehmehri; Saeed Nasrollahi; Morteza Aliyari
Abstract
In this paper, a model predictive control approach based on a generic particle filter is proposed to synchronize two Josephson junction models with different parameters. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function ...
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In this paper, a model predictive control approach based on a generic particle filter is proposed to synchronize two Josephson junction models with different parameters. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using a generic particle filter. Here, Josephson junction is described with Resistive Capacitive Inductive Shunted Josephson model, and the synchronization is obtained using the slave–master technique. Moreover, to verify the implementation capability of the proposed algorithm, a processor in loop experiment is performed. The results show that the open-loop system, without the controller, has a chaotic behavior. Numerical simulations are conducted to assess the performance of the proposed algorithm. The results show that the proposed approach can be implemented in a real-time application. Also, the performance of the suggested controller is compared with the proportional integral derivative controller and sliding mode controller.
Control
Rohollah Hasanzadeh Fereydooni; Hassan Siahkali; Heidar Ali Shayanfar; Amir Hooshang Mazinan
Abstract
Nowadays, rehabilitative robots that have received more attention in the field of rehabilitation can help patients in the rehabilitation training and reduce therapist workload. This paper suggests the use of surface electromyography (sEMG) signals and a bidirectional neural network (BRNN) for the estimation ...
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Nowadays, rehabilitative robots that have received more attention in the field of rehabilitation can help patients in the rehabilitation training and reduce therapist workload. This paper suggests the use of surface electromyography (sEMG) signals and a bidirectional neural network (BRNN) for the estimation of the joint angles of lower limbs. The input of BRNN is the preprocessed sEMG signals and its outputs are the estimated joint angles of knee, ankle and hip. In order to prove the usefulness of the bidirectional neural network, four normal and healthy subjects and two spinal cord injury (SCI) patients take a part in the experimental tests. The healthy subjects exercise two movement modes containing the leg extension and the treadmill at various loads and speeds, while the SCI subjects conduct only the treadmill exercise. In order to record useful information, seven leg muscles were used and then the hip, knee and ankle joint angles were acquired at the same time. The experimental results show the satisfactory performance of the proposed method in the estimation of joint angles by employing surface electromyography signals for both groups. The proposed estimation method can be used for controlling the rehabilitation robot of SCI subjects based on sEMG signals.
Control
Saeid Haidari; Hadi Moradi; Seyyed.M.M Dehghan
Abstract
In this paper, RF source localization in non-line of sight condition, using map of the obstacles is proposed. Received signal strength indicator (RSSI) and angle of arrival (AOA) measurements are observations which are obtained from received signal on the UAV. In the proposed approach, AOA are used to ...
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In this paper, RF source localization in non-line of sight condition, using map of the obstacles is proposed. Received signal strength indicator (RSSI) and angle of arrival (AOA) measurements are observations which are obtained from received signal on the UAV. In the proposed approach, AOA are used to determine the obstacle on the map from which the reflection has happened. Then the RSSI information is used to determine the location of the RF source. In the basic version of approach, triangulation is used to determine the location of the RF source. In the advanced approach, the reflection angle is also estimated to improve the localization accuracy. The estimation is done using particle filter approach. In addition, it is shown analytically that the maximum localization error for the advanced approach is bounded but relative formation of the reflectors with respect to each other can increase the localization error for the basic approach.
Control
Bahareh Bidar; Mir Mohammad Khalilipour; Farhad Shahraki; Jafar Sadeghi
Abstract
The changes in the crude oil flow rate to an atmospheric distillation unit can influence the quality of the products. This paper presents a modification method for soft sensing model including an update term, which makes it compatible with industrial variations. A modified soft sensing structure is adopted ...
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The changes in the crude oil flow rate to an atmospheric distillation unit can influence the quality of the products. This paper presents a modification method for soft sensing model including an update term, which makes it compatible with industrial variations. A modified soft sensing structure is adopted using lookup table (LUT) method where steady-state soft sensing models are performed. The steady-state soft sensing models is proposed based on local instrumental variable (LIV) technique for an industrial atmospheric distillation unit (ADU) at Shiraz refinery, Iran. The LIV-based soft sensors are utilized tray temperature measurements to monitor ASTM-D86 index of side products for nominal flow rate (60,000bbl/day). Lookup tables have been developed based on the difference between the predicted values of ASTM-D86 index and corresponding simulation values to make update terms in different feed flow rates. The results present improvement in the predictions of LIV-based soft sensors as well as acceptable control performance in feed flow rate variations. The comparison of soft sensing results with/without lookup tables demonstrates that the proposed update term helps to predict product quality more precisely and is suitable for advanced monitoring scheme due to no complexity and low computational time.
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 .
