Control
alireza khoshsoadat; mohamad Abedini; mohammad reza mirzaei
Abstract
Objective: Three-phase boost rectifier is a Voltage-Source Converter that converts three-phase AC input voltage to a higher DC voltage. In this paper, an artificial intelligent-based system, with learning and adapting ability, is designed for using in the two voltage-based control methods of rectifiers, ...
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Objective: Three-phase boost rectifier is a Voltage-Source Converter that converts three-phase AC input voltage to a higher DC voltage. In this paper, an artificial intelligent-based system, with learning and adapting ability, is designed for using in the two voltage-based control methods of rectifiers, with the names of Voltage Oriented Control (VOC) and Direct Power Control (DPC). For implementation of this intelligent controller a hybrid structure of the Fuzzy Logic (FL) and Neural Networks (NN) that named as Adaptive Network-based Fuzzy Inference System (ANFIS) is used. Among the common network training algorithms, the error back propagation algorithm is known as the most common solution by providing an efficient computational method, so in this article, the above method is used to design the controller. This neuro-fuzzy-based control model is applicable in both VOC and DPC methods and increases the correctness of the output current and DC voltage with low ripple, short settling time and also dynamic operation. The implementation of the proposed controller is simple and requires only 49 fuzzy rules. Compared to other controllers whose structure is neural network and fuzzy, it has fewer layers and its accuracy is higher.
Control
Farshid Aazam Manesh; Elham Amini Boroujeni; Fateme Bazarkhak; Mahdi pourgholi
Abstract
In this paper, an observer-based controller design for fractional-order multi-agent systems is discussed. By introducing a novel algorithm and leveraging appropriate lemmas and theoretical frameworks, we propose a stable observer and a distributed consensus protocol tailored for multi-agent systems within ...
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In this paper, an observer-based controller design for fractional-order multi-agent systems is discussed. By introducing a novel algorithm and leveraging appropriate lemmas and theoretical frameworks, we propose a stable observer and a distributed consensus protocol tailored for multi-agent systems within the Lipschitz and one-sided Lipschitz classes of nonlinear systems. Lipschitz systems have a bounded rate of change, ensuring proportional output to input differences, while one-sided Lipschitz systems relax this constraint, allowing differential growth in one direction for efficiency. The stability of the observer and the controller in achieving the consensus problem is demonstrated using the Lyapunov's second method. The proposed approach is rigorously developed, ensuring that the designed observer and controller meet the necessary stability criteria. Extensive simulation results validate the theoretical findings, showcasing the method's effectiveness and robustness in practical scenarios. Specifically, the simulations demonstrate that the proposed method achieves global Mittag-Leffler stability, with the estimated states converging to the actual states with minimal deviation. The method's advantages include its ability to handle a broader class of nonlinear systems, including those with large Lipschitz constants, and its robustness to uncertainties and nonlinearities. These simulations confirm the theoretical predictions and illustrate the practical applicability of our approach in real-world multi-agent systems, such as swarm robotics, power grids, and sensor networks.
Control
FARIBA Nobakht; Hussein Eliasi
Abstract
This paper proposes a robust adaptive control strategy based on integral backstepping for nonlinear epidemic systems under input saturation, model uncertainties, and external disturbances. The proposed method combines backstepping for systematic control design, sliding mode control for robustness, and ...
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This paper proposes a robust adaptive control strategy based on integral backstepping for nonlinear epidemic systems under input saturation, model uncertainties, and external disturbances. The proposed method combines backstepping for systematic control design, sliding mode control for robustness, and adaptive control to handle unknown parameters dynamically. To address input saturation, a novel auxiliary design system combined with Nussbaum gain functions is introduced, mitigating saturation effects and ensuring stability. The epidemic dynamics are modeled using the SEIAR framework, which includes Susceptible, Exposed, Infected, Asymptomatic, and Recovered populations. The controller employs three control inputs—vaccination, social distancing measures, and treatment of infected individuals—to drive the populations of susceptible, exposed, and infected individuals to zero. Simulation results demonstrate that the proposed control scheme effectively eliminates infections, ensuring that the recovered population converges to the total population size. The method provides precise trajectory tracking despite input constraints and external disturbances. These findings highlight its strong potential for real-world epidemic management, particularly in resource-limited and uncertain environments.
Control
Yunes Mohamadi; Maryam Alipour; Akbar Hashemi Borzabadi
Abstract
The present paper proposes a novel numerical approach for approximating solutions to optimal control problems with parabolic constraints. Utilizing Laguerre polynomials as a novel basis set, a method was developed to address a class of this problem. The employment of these basis functions in conjunction ...
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The present paper proposes a novel numerical approach for approximating solutions to optimal control problems with parabolic constraints. Utilizing Laguerre polynomials as a novel basis set, a method was developed to address a class of this problem. The employment of these basis functions in conjunction with the collocation method facilitates the transformation of optimal control problems governed by parabolic constraints into a system of nonlinear algebraic equations. The present study proposes an efficient discretization and transformation of complex optimal control problems governed by parabolic equations into lower-dimensional algebraic systems by leveraging the unique properties of Laguerre polynomials.Convergence analysis has been demonstrated to ascertain the optimal value approximations of the proposed method. In order to provide a comprehensive illustration of the reliability and applicability of the proposed method, two illustrative examples are presented. The findings underscore the efficacy and precision of the implemented methodology. This work makes a significant contribution to the field by offering a robust framework for solving complex parabolic control problems, thereby demonstrating the potential of spectral methods in the context of optimal control theory.
Control
Nima Rajabi; Ramezan Havangi; Amir Hossein Abolmasoumi
Abstract
An earthquake is a sudden and destructive natural disaster that often results in unpredictable damage to human life and property. Investigating the effects of earthquakes on buildings and enhancing the seismic performance of structures is a crucial approach for mitigating severe damage ...
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An earthquake is a sudden and destructive natural disaster that often results in unpredictable damage to human life and property. Investigating the effects of earthquakes on buildings and enhancing the seismic performance of structures is a crucial approach for mitigating severe damage during such events. One effective tool in testing the resistance of structures against earthquakes is the use of shaking tables. In this paper, the stabilization and control of earthquake simulator using a fuzzy sliding mode controller (FSMC) and adaptive unscented Kalman filter (AUKF)and adaptive extended Kalman filter (AEKF) is presented. These filters employ a recursive technique to effectively adjust the noise covariance by utilizing an adaptation method known as the steepest descent. In the proposed approach, the shaking table states are estimated using an accelerometer, encoder, and camera. These estimated states are then utilized by the AEKF/AUKF to stabilize and control the closed-loop system. A fuzzy sliding mode controller is designed to track the reference input, and eliminate external disturbances and noise. In the control of sliding mode, the occurrence of chattering in the control input is unavoidable. To mitigate this undesired chattering phenomenon, a fuzzy inference mechanism has been employed. The image processing approach has been utilized to measure the displacement online using the camera. The advantages of using the camera include not requiring direct contact with the table, as well as offering a low price and good accuracy.
Control
Ramezan Havangi; fatemeh karimi
Abstract
Accurate estimation of State of Charge (SOC) is essential for the efficiency, safety, and durability of battery-powered devices, playing a vital role in Battery Management Systems (BMS). This paper introduces an innovative method combining the Adaptive Robust Square Root Unscented Kalman Filter (ARSRUKF) ...
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Accurate estimation of State of Charge (SOC) is essential for the efficiency, safety, and durability of battery-powered devices, playing a vital role in Battery Management Systems (BMS). This paper introduces an innovative method combining the Adaptive Robust Square Root Unscented Kalman Filter (ARSRUKF) with Recursive Least Squares (RLS) to enhance SOC estimation accuracy and robustness. By maintaining semi-positive definite covariance matrices, the proposed method ensures numerical stability, avoiding issues commonly encountered in traditional techniques. A key feature of the ARSRUKF is its direct computation of the square roots of covariance matrices, preserving their symmetry and positive definiteness while increasing computational efficiency. Unlike conventional filters, such as the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), the ARSRUKF operates effectively without prior knowledge of noise statistics, accommodating non-Gaussian noise or uncertain noise characteristics in real-world scenarios.To further improve performance, an Adaptive Neuro-Fuzzy Inference System (ANFIS) dynamically tunes noise covariances in real-time, adapting to changes in operating conditions like temperature variations, battery aging, and load shifts. Extensive experimental results highlight the superior performance of the ARSRUKF compared to the EKF and UKF, particularly in conditions with unknown or varying noise statistics. This approach demonstrates significant advancements in SOC estimation accuracy, stability, and consistency. The proposed method has broad potential applications in electric vehicles, renewable energy storage, and portable electronics, offering a robust and efficient solution for advanced battery systems.
Control
Violet Farhad; Seyed Mehdi Mirhosseini-Alizamini
Abstract
This paper introduces a new application of variable gain sliding mode control (VGST) to the air supply system of a proton exchange membrane fuel cell (PEMFC), which is crucial for its performance and longevity. The air supply system comprises a centrifugal compressor, a DC-DC converter, and a fuel cell ...
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This paper introduces a new application of variable gain sliding mode control (VGST) to the air supply system of a proton exchange membrane fuel cell (PEMFC), which is crucial for its performance and longevity. The air supply system comprises a centrifugal compressor, a DC-DC converter, and a fuel cell stack, forming a complex and nonlinear system with multiple inputs and outputs. The VGST method adjusts the control gain based on the system state and the sliding level and employs a cascade structure to regulate the excess oxygen ratio and the compressor airflow. The main goals of VGST are to control the PEMFC output voltage and power under various load conditions and uncertainties and to optimize the excess oxygen ratio (λ_(O_2 )) to avoid oxygen depletion and membrane damage. The stability and robustness of the proposed controller are verified by Lyapunov theory and its performance is superior compared to other controllers such as variable gain closed-loop control and constant gain sliding mode control (single loop and cascade). The controller is validated by simulation and experimental data and demonstrates that it can enhance the efficiency and reliability of the PEMFC system. The variable gain controller of the cascade structure was also tested under noisy and uncertain conditions to further confirm its desired performance and showed that it could cope well with adverse situations and achieve the control objectives.
Control
Mohsen Hamzeh; Sepehr Shakibi; Amir Mohammad Farahani
Abstract
Photovoltaic (PV) systems are a backbone of the infrastructure of renewable energy with its usage growing significantly. Early fault detection of these systems being essential to enhance their reliability and efficiency. Despite the development of fault diagnosis methods of PV system promoted by ...
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Photovoltaic (PV) systems are a backbone of the infrastructure of renewable energy with its usage growing significantly. Early fault detection of these systems being essential to enhance their reliability and efficiency. Despite the development of fault diagnosis methods of PV system promoted by machine learning models such as ensemble learning, support vector machine and neural networks, challenges in achieving high accuracy and generalization persist. In this paper, propose a deep learning method based on a ResNet architecture for reliable and efficient fault detection, including the following categories: Normal Operation, Short-Circuit, Degradation, Open Circuit, and Shadowing. Also devise a new learning rate schedule(LRS), which considerably improves the training dynamics and enables a 63% improvement in model performance. The suggested method has excellent performance achieves 99.8% accuracy throughout the training, validation and testing phases. The results obtained showcase the potential of ResNet-based architectures, in addition to prowess in adaptive learning rate strategies, at enhancing the reliability of photovoltaic systems through scalable and precise fault diagnosis.
Control
Mohammad Moodi; Mohammad Reza Ramezani-al
Abstract
Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in electric vehicles and energy storage systems. This paper proposes a fusion-based SOC estimation method that integrates two extended Kalman filters (EKFs), each paired with a distinct ...
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Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in electric vehicles and energy storage systems. This paper proposes a fusion-based SOC estimation method that integrates two extended Kalman filters (EKFs), each paired with a distinct open-circuit voltage (OCV)–SOC model. The fusion strategy, grounded in Bayesian probability and residual error analysis, dynamically assigns weights to each model’s output, ensuring that the most appropriate model contributes predominantly to the final SOC estimate at any given moment. The proposed framework utilized a second-order equivalent circuit model (ECM) and estimates parameters online via a variable forgetting factor recursive least squares (VFFRLS) algorithm. Simulation results under LA92 and UDDS driving cycles demonstrate that the method achieves superior accuracy and robustness, reducing the maximum estimation error by up to 26% and RMSE by over 10% compared to conventional EKF approaches. These findings highlight the method’s effectiveness and adaptability for real-time battery management applications.
Control
Ali Madady; Naser Taghva Manesh
Abstract
This paper introduces a novel optimal iterative learning control scheme for continuous-time systems with multiple-inputs and multiple-outputs and linear time-varying dynamics. While iterative learning control has been extensively studied in the discrete-time domain, the development of optimal iterative ...
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This paper introduces a novel optimal iterative learning control scheme for continuous-time systems with multiple-inputs and multiple-outputs and linear time-varying dynamics. While iterative learning control has been extensively studied in the discrete-time domain, the development of optimal iterative learning control for continuous-time systems remains limited due to the lack of lifted-formulations and associated mathematical challenges. The proposed method transforms the original optimal iterative learning control problem into a linear quadratic tracking-like problem, enabling the derivation of an explicit close-loop control law that ensures both tracking performance and control effort minimization. Unlike many existing approaches that rely on learning algorithms involving derivative terms, which are often sensitive to measurement noise, the proposed design avoids such terms and remains computationally efficient. Moreover, the monotonic convergence of the tracking error and the associated cost function are proved by rigorous mathematical analysis. Theoretical results are supported by four comprehensive simulation examples, including comparisons with several existing iterative learning control methods. Quantitative evaluations confirm that the proposed optimal scheme significantly outperforms previous techniques in terms of convergence speed and error reduction rate. This contribution offers a new framework for the optimal control of continuous-time systems with multiple inputs and outputs in repetitive tasks and provides a foundation for future extensions to constrained, nonlinear, or partially measurable systems.
Control
Maryam Shahriari; Seyed Hojat Nourian
Abstract
This work proposes an adaptive resilient control for uncertain nonlinear cyber-physical systems (CPSs) under deception attacks. It is assumed that attacker injects false data into the commands exchanged between the controller and actuator over the communication channels. The injected false data affects ...
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This work proposes an adaptive resilient control for uncertain nonlinear cyber-physical systems (CPSs) under deception attacks. It is assumed that attacker injects false data into the commands exchanged between the controller and actuator over the communication channels. The injected false data affects the control input in both additive and multiplicative forms. To deal with the uncertain dynamics of the system and additive term of cyber-attacks, the radial basis function-neural networks (RBF-NNs) are invoked. Also, to handle adverse effects of multiplicative term of cyber-attack, the Nussbaum-type gain function is employed. Then, by integrating the RBF-NN model and Nussbaum function into the command filtered backstepping (CFB) approach, the proposed resilient control scheme is designed. Compared with the existing works, the proposed control eliminates the “explosion of complexity” problem in the conventional backstepping approach, removes the trial and error in choosing time constant of the first order filters in the dynamics surface control (DSC) approach, compensates the filtering error and deals with both additive and multiplicative cyber-attacks in “controller to actuator” channel, simultaneously. Also, it mitigates the effects of the cyber-attack without requiring separate attack estimation unit, controller reconfiguration or readjustment algorithm. Simulation results on the robotic arm under different cyber-attacks verify effective resilient performance of the proposed control scheme.
Control
Mohammadrasol Hajali; Ramezan Havangi
Abstract
In the field of mobile robot navigation, challenges such as nonlinear conditions, uncertainties, and the development of advanced methods have necessitated accurate position estimation. In this paper, fuzzy based adaptive unscented Kalman filter (FAUKF) is evaluted to enhance the state estimation ...
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In the field of mobile robot navigation, challenges such as nonlinear conditions, uncertainties, and the development of advanced methods have necessitated accurate position estimation. In this paper, fuzzy based adaptive unscented Kalman filter (FAUKF) is evaluted to enhance the state estimation performance for mobile robot localization. In proposed method, the FAUKF algorithm effectively removes the noise uncertainty by adaptively adjusting the covariance of the measurement noise according to the adaptation law. The output of the Mamdani Fuzzy Inference System (FIS) acts as an observer and improves the matching law. The results of this research show the accuracy of FAUKF algorithm position estimation compared to traditional UKF methods. Also, this study presents a pioneering navigation strategy for mobile robots, which integrates random tree routing algorithm with rapid exploration (RRT*) for optimal path design in indoor environments. The goal of RRT* integration is to create optimal routes taking into account safety and environmental constraints. By combining the Kalman filter prediction and updating steps, this method reduces the accumulation of uncertainty during the localization process and facilitates accurate localization and path planning towards the target.The simulation results confirm the effectiveness of this method in keeping the uncertainty levels in localization constant over time. The presented adaptive method enables efficient navigation in complex environments. Path planning is a critical element in robotics applications and the RRT* based approach presented in this paper provides a comprehensive solution to create optimal and efficient paths.
Control
Hossein Zahmatkesh; Hussein Eliasi
Abstract
State estimation of nuclear reactors often plays a crucial role in accomplishing load-following control. This study presents a novel approach that leverages a weighted particle filter to address the challenges associated with estimating these crucial parameters, including relative precursor concentration ...
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State estimation of nuclear reactors often plays a crucial role in accomplishing load-following control. This study presents a novel approach that leverages a weighted particle filter to address the challenges associated with estimating these crucial parameters, including relative precursor concentration (C_r) and fuel temperature (T_f), under varying reactor power conditions. A high-fidelity nonlinear dynamic reactor model was developed, incorporating noises in both process and measurement models. The proposed method was evaluated by extensive simulations under a wide range of operational scenarios. The particle filter demonstrated exceptional performance in tracking the time-varying states of the nuclear reactor. Comparative analysis with a conventional Kalman filter and the extended Kalman filter revealed the superior robustness of the particle filter in handling nonlinearities inherent in nuclear systems. The proposed approach offers several advantages, including the ability to capture multimodal distributions, handle non-Gaussian noise, and provide probabilistic estimates. Despite the increased computational cost associated with particle filtering, the benefits in terms of estimation accuracy and reliability justify its application in nuclear power plant monitoring and control systems.
Control
Mahmood Nazifi; Mahdi Pourgholi
Abstract
The consensus of Cyber-Physical Power Systems (CPPSs), where generators agree on common desired rotor angles and speeds, is vital for maintaining system stability and efficiency. This study explores this consensus using fractional-order multi-agent systems, offering advantages over traditional methods. ...
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The consensus of Cyber-Physical Power Systems (CPPSs), where generators agree on common desired rotor angles and speeds, is vital for maintaining system stability and efficiency. This study explores this consensus using fractional-order multi-agent systems, offering advantages over traditional methods. CPPSs often encounter issues like faults, uncertainties, disturbances, and cyber-attacks. To address these, a new Adaptive Fractional-Order Sliding Mode Controller (AFOSMC) is proposed, designed to achieve consensus despite unknown nonlinear functional upper bounds characterizing system perturbations. The AFOSMC uses stable adaptive laws to determine these unknown coefficients, ensuring robust performance even under adverse conditions. It outperforms conventional Integer-Order counterparts by reducing chattering and enabling faster convergence during the initial phase of CPPS operations. The AFOSMC also ensures finite-time convergence to the sliding surface, enhancing system responsiveness and stability. The controller's stability is rigorously proven using Lyapunov's theorem. Finally, extensive simulations demonstrate the practical benefits of the AFOSMC, and comparisons with recent research highlight its superior performance in robustness and efficiency.
Control
Fatemeh Soleimannouri; Saeed Khorashadizadeh; Mohsen Farshad; Naser Mehrshad
Abstract
In this research, an adaptive fuzzy controller is presented to regulate the blood glucose level of type 1 diabetic patients in the presence of input saturation. This controller along with an adaptive anti-windup compensator is considered to deal with the uncertainty of the Bergmann minimal nonlinear ...
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In this research, an adaptive fuzzy controller is presented to regulate the blood glucose level of type 1 diabetic patients in the presence of input saturation. This controller along with an adaptive anti-windup compensator is considered to deal with the uncertainty of the Bergmann minimal nonlinear model parameters as well as the input saturation. Anti windup compensator is designed to prevent to saturation problems as hyperglycemia or hypoglycemia in regulating the blood glucose level of type 1 diabetes patients. The Bergman minimal model is used to mathematically model type 1 diabetes, depicting the dynamic behavior of the human body's blood glucose-insulin system. In the first step, the stability of the closed-loop system has been theoretically investigated and proved from the point of view of Lyapunov's theory. Next, to evaluate the effectiveness of the proposed method in regulating blood glucose levels, the proposed control system has been implemented in the presence of meal disturbances using the Simulink environment of MATLAB software. The implementation results show a lower control effort and less convergence time of the proposed method compared to the existing methods.
Control
Seyedeh Mahsa Zakipour Bahambari; Saeed Khankalantary
Abstract
This article focuses on the design of a controller for quadcopter position control, which is then used to organize a group of quadcopters into a specific formation. Initially, PID controllers are developed to manage all output variables of the quadcopter systems efficiently. Subsequently, a constrained ...
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This article focuses on the design of a controller for quadcopter position control, which is then used to organize a group of quadcopters into a specific formation. Initially, PID controllers are developed to manage all output variables of the quadcopter systems efficiently. Subsequently, a constrained tube-model predictive control (Tube-MPC) approach is implemented to regulate the system's position, comparing its performance to that of the tube-MPC controller. The article also explores the coordination of a group of six quadcopters, focusing on achieving a predefined formation that maintains the desired shape. Three different scenarios are examined to control the formation, assessing how each approach influences the arrangement and coordination of the quadcopters. The dynamics of the system's control are crucial for effective operation in multi-agent systems. Moreover, the configuration of the quadcopters is influenced by each quadcopter's internal controller, ensuring accurate formation and tracking. This study underscores the significance of sophisticated control strategies in improving the performance and coordination of multiple quadcopter systems.
Control
Mir Mohammad Khalilipour; Farhad Shahraki; Jafar Sadeghi; Kiyanoosh Razzaghi
Abstract
Objective: The objective of this research is to optimize the crude distillation unit (CDU) in oil refineries by reducing energy consumption and improving operational efficiency through the application of a Proportional-Integral-Plus (PIP) control system within a Non-Minimal State Space (NMSS) framework. ...
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Objective: The objective of this research is to optimize the crude distillation unit (CDU) in oil refineries by reducing energy consumption and improving operational efficiency through the application of a Proportional-Integral-Plus (PIP) control system within a Non-Minimal State Space (NMSS) framework. Material and Method: Simulations of the CDU were carried out using Aspen Plus for modeling the distillation process and MATLAB for implementing the PIP control structure. The controller was tuned by an economic cost function, optimizing key parameters such as furnace duty, side-draw rates, and condenser heat removal. The PIP control system was compared to traditional control methods, with performance evaluated under various disturbances, including feed rate, temperature, and composition changes. Results: The PIP control strategy significantly improved the CDU’s performance, reducing operating costs by up to 100% compared to traditional control methods optimized by the Integral of Time-weighted Absolute Error (ITAE). The PIP system demonstrated superior disturbance handling and energy efficiency while maintaining product quality. Conclusion: The findings indicate that the PIP control system is a highly effective tool for optimizing energy consumption and process stability in modern refineries, especially under fluctuating operational conditions. Its application could lead to substantial cost savings and improved efficiency in CDU operations.
Control
Valiollah Ghaffari
Abstract
Employing discrete-time techniques, the min-time control of continuous-time dynamical systems is mainly studied through an analytical framework. To this aim, the exact discrete-time model of the linear time-invariant systems is specified through a zero-order hold. The optimal solution could be directly ...
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Employing discrete-time techniques, the min-time control of continuous-time dynamical systems is mainly studied through an analytical framework. To this aim, the exact discrete-time model of the linear time-invariant systems is specified through a zero-order hold. The optimal solution could be directly determined from some necessary conditions. However, the structure of the optimum control sequences is derived by utilizing the well-known Pontryagin principle. Employing the state transition matrix, the states of the control system are computed at the switching times. The switching times of the control signal would be found from a set of nonlinear algebraic equations. Accordingly, the transformation of the system’s states, from a known initial point to a specific value, would be accomplished in the minimum possible time. Applying the proposed scheme, the exact (integer) values of the switching times and the final time are numerically determined from the solution of an algebraic equation. Several discrete-time and continuous-time examples are discussed and simulated to show the feasibility and effectiveness of the suggested procedure in the dynamical systems. The simulation results confirm the method’s advantages over the existing ones.
Control
Sorush Akhlaghi Amiri; Naser Pariz; Mohammad Bagher Naghibi Sistani
Abstract
In this paper, we propose the problem of connectivity maintenance for a group of robots that are connected with wireless communication. The communication between the agents is modeled by a fading channel, and the exchange of information is possible at permissible distances. The distributed controller ...
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In this paper, we propose the problem of connectivity maintenance for a group of robots that are connected with wireless communication. The communication between the agents is modeled by a fading channel, and the exchange of information is possible at permissible distances. The distributed controller is designed to maintain the global connectivity of the network communication graph. On the other hand, obtaining the best communication quality between agents is the definition of good network connectivity. Therefore, the Laplacian matrix is defined as a weighted graph according to the communication parameters. Initially, the controller uses the supergradation algorithm to remake the network Laplacian matrix for having a bigger second eigenvalue. While each agent in the network only has access to the neighbor's information. The control algorithm uses a multi-agent estimator to find the eigenvector corresponding to the second small eigenvalue. The formation problem has been considered in the second step after the reference topology has been obtained. Because of the nonlinear dynamics of the agents, the sliding mode controller has been used for this purpose. This robust controller could be a suitable choice due to the modeling uncertainty and sensor measurement uncertainty. Finally, an example of multi-agent robots is provided to evaluate the algorithm.
Control
Morteza Janfaza; Abbas-Ali Zamani
Abstract
A new framework for controlling load frequency in a complex, interconnected power system with multiple sources has been developed. This framework combines a fuzzy logic controller (FLC) and a tilted integral derivative (TID) controller, creating a self-tuning fuzzy tilted integral derivative (STFTID) ...
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A new framework for controlling load frequency in a complex, interconnected power system with multiple sources has been developed. This framework combines a fuzzy logic controller (FLC) and a tilted integral derivative (TID) controller, creating a self-tuning fuzzy tilted integral derivative (STFTID) controller. The purpose of this controller is to conduct and reduce load frequency perturbations during the operation of a multi-area interconnected multi-source power system. The STFTID controller is optimized using a particle swarm optimization algorithm to minimize the frequency fluctuations effectively. Investigations of the proposed STFTID controller were performed for power systems with generation units of a conventional system and renewable energy sources. In the design process of the STFTID controller, various nonlinearities, uncertainties, and fluctuations are considered to simulate practical challenges. These challenges include generation rate constraints, governor deadband, and communication time delays (as the sources of nonlinearity), as well as fluctuations caused by step load switching and the connection of renewable power plants to the system. The STFTID controller is compared with the proportional integral derivative (PID), titled integral derivative (TID), and integral tilted-derivative (I-TD) controllers. Simulation results show that the developed STFTID controller significantly enhances the system frequency control under various applied conditions, including multi-step load perturbation, renewable power plant integration, communication time delays, and generation rate constraints.
Control
Ehsan Moshksar
Abstract
Deriving an accurate and simple current-voltage (I-V) characteristic for photovoltaic (PV) module is highly significant for condition monitoring, fault detection and maximizing power production in PV systems. Equivalent circuits consist of one or more diodes are mostly utilized for I-V curve modelling. ...
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Deriving an accurate and simple current-voltage (I-V) characteristic for photovoltaic (PV) module is highly significant for condition monitoring, fault detection and maximizing power production in PV systems. Equivalent circuits consist of one or more diodes are mostly utilized for I-V curve modelling. However, these models are inherently implicit, relatively complex and nonlinear in their parameters. Here, a piecewise quadratic function with four different intervals is generated from the measured I-V data at standard test condition (STC). The intervals are chosen such that the best model performance can be achieved, especially at maximum power point. Each quadratic function is obtained from least square technique according to the experimental data in the corresponding interval. It is easy to obtain the voltage value at MPP from the extracted model, analytically. Also, a suggestion is provided for extending the generated I-V model to the real environmental condition by utilization of artificial neural network. The derived PV module model is highly suitable for maximum power point tracking, monitoring and fault detection due to its simplicity, explicit structure and accuracy.
Control
Gholam Reza Shahabadi; Majid Reza Naseh
Abstract
A type of converter called Quasi-Z-source converters (QZSC) is becoming more popular due to its benefits, such as operating in a single stage, having smaller components, and maintaining continuous input current and a common ground. This converter is widely used in various applications that need a DC-DC ...
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A type of converter called Quasi-Z-source converters (QZSC) is becoming more popular due to its benefits, such as operating in a single stage, having smaller components, and maintaining continuous input current and a common ground. This converter is widely used in various applications that need a DC-DC converter. The small-signal analysis and linearization method are often used to control the QZSC. The linear model of QZSC does not provide sufficient stability control over a wide range. Sliding Mode Control (SMC) has become widely used for electronic power converters because of their variable structure .This paper presents a SMC for a QZSC with three objectives:1) to achieve stability across a wide range of QZSC; 2) to systematically select the proposed controller coefficients; and 3) to enable tracking of the reference voltage in spite of changes in input voltage, reference voltage, and output load. The simulations have been done with the help of MATLAB/Simulink and show the effectiveness of the proposed method.
Control
Mohammed Yakoob; Mina Salim; Amir A. Ghavifekr
Abstract
Regulating voltage and current signals in microgrids (MG) is essential to ensure stability, optimize power quality, support grid integration, enhance operational efficiency, and promote safety within the system. This paper introduces a novel Linear Matrix Inequalities (LMI)-based approach aimed at regulating ...
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Regulating voltage and current signals in microgrids (MG) is essential to ensure stability, optimize power quality, support grid integration, enhance operational efficiency, and promote safety within the system. This paper introduces a novel Linear Matrix Inequalities (LMI)-based approach aimed at regulating voltage and current signals within microgrids through the utilization of sliding mode control. The MG under examination in this paper is composed of a voltage source inverter (VSI) for DC to AC voltage conversion, a filter to ensure sinusoidal signal quality, and an array of loads, including those with uncertain characteristics. The objective of this study is to regulate the output voltage and current in a short period of time in the presence of diverse loads. By promptly adjusting voltage and current levels, the microgrid can effectively accommodate fluctuations in demand and maintain optimal performance under changing conditions. The presented controller consists of two parts: a state feedback gain calculated from the LMI and a sliding mode-based controller to maintain system stability. This controller is intended to reject disturbances, track reference signals, and minimize steady-state errors in a limited time. The satisfactory performance of the microgrid will have a significant impact on various parameters, such as frequency, active power, reactive power, and power factor. Simulating the voltage source inventor and presenting numerical results demonstrate the effectiveness of the proposed controller to provide high robustness against uncertainty and nonlinear loads while maintaining system stability.
Control
Amir Rezaie
Abstract
Objective: In this article, time-varying chaotic systems with uncertainties, including external disturbances, are considered, and sliding mode control (SMC) is used to control such systems. To control these systems, an autonomous differential equation is first introduced. Then, based on this differential ...
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Objective: In this article, time-varying chaotic systems with uncertainties, including external disturbances, are considered, and sliding mode control (SMC) is used to control such systems. To control these systems, an autonomous differential equation is first introduced. Then, based on this differential equation, a sliding surface is defined to control this chaotic system. This kind of controller is remarkable in that it removes the effects of disturbances, whether bounded or unbounded. Therefore, the system is known to be fixed-time stable. where the trajectories of this chaotic system are not placed on the sliding surface, we have created creative controllers to place the trajectories on the sliding surface in finite time. Theoretical investigations show that such chaotic systems can be made fixed-time stable by applying the controls proposed in this study. Based on the findings of this study, the controllers are designed to eliminate all disturbances, whether bounded or unbounded. the results be said to apply chaotic, time-dependent, and time-independent systems. To further consolidate the results obtained in this article, two examples, namely the time-dependent system of the Gyro and the time-independent system of the Liu, are investigated, and the results were compared with previous works by other researchers.
Control
Mohammad Shahi; Mohammad Reza Sohrabi; Sadegh Etedali; Abbas-Ali Zamani
Abstract
This research proposes an innovative process to locate devices in elevation using structural results in uncontrolled and controlled (passive and active) states, considering Soil-Structure Interaction (SSI) effects, especially for soft soil. Also, a Proportional Integral Derivative (PID) controller with ...
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This research proposes an innovative process to locate devices in elevation using structural results in uncontrolled and controlled (passive and active) states, considering Soil-Structure Interaction (SSI) effects, especially for soft soil. Also, a Proportional Integral Derivative (PID) controller with active single and multiple control devices is used for tall buildings under earthquakes. In addition, the simultaneous and non-simultaneous tuning of the design parameters are examined. The results of applying PID with a Multiple Active Tuned Mass Damper (MATMD) compared with the Single-Active Tuned Mass Damper (SATMD) show that the proposed process of locating the control devices reduces responses significantly. It also reduces the computational efforts of the optimization noticeably. The results of the non-simultaneous tuning of design parameters in all states also indicate an increase in the instability potential of the structure compared with simultaneous tuning. On the other hand, the reduction of the Root Mean Square (RMS) of the responses compared with the uncontrolled state confirms the effective performance of the system during earthquakes. Therefore, this research helps researchers gain a new design vision of how to locate control devices in tall buildings without optimization calculations and how to set parameters in the presence of SSI effects.
