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
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.
Power systems
Ramezan Havangi; Fatemeh Karimi
Abstract
Battery Management System (BMS) including measurements errors that causes decrease in the quality of calculated State of the Charge (SOC). It will limit the accurate estimation of the SOC that is a critical challenge in some of the engineering fields such as medical science, robotics, ...
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Battery Management System (BMS) including measurements errors that causes decrease in the quality of calculated State of the Charge (SOC). It will limit the accurate estimation of the SOC that is a critical challenge in some of the engineering fields such as medical science, robotics, navigation and industrial applications. These facts implies on the significance of SOC estimation from battery measurements that is the matter of the literature through the recent years. Due to the dependency of the EKF to the system model, the change in the battery parameters and noise information cause losing performance in the SOC estimation over the time. In this paper, we assume that the battery parameters including internal resistance and capacitor and also the noise information are varying over the time. To solve that, two separate on-line identification algorithms for parameters and noise information are introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify the resistance and capacitor values. Moreover, the process and measurement noise covariance are estimated based on iterative noise information identification algorithm. Then all of the updated values are used in the EKF algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model parameters. To address the challenge of uncertain model parameters, RLS is introduced.
Control
Ramezan Havangi; Maryam Moradi
Abstract
An ideal traction and braking system not only ensures ride comfort and transportation safety but also attracts significant cost benefits through reduction of damaging processes in wheel-rail and optimum on-time operation. In order to overcome the problem of the wheel slip/slide at the wheel-rail contact ...
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An ideal traction and braking system not only ensures ride comfort and transportation safety but also attracts significant cost benefits through reduction of damaging processes in wheel-rail and optimum on-time operation. In order to overcome the problem of the wheel slip/slide at the wheel-rail contact surface, detection of adhesion and its changes has high importance and scientifically challenging, because adhesion is influenced by different factors. However, critical information this detection provides is applicable not only in the control of trains to avoid undesirable wear of the wheels/track but also the safety compromise of rail operations. The adhesion level between the wheel and rail cannot be measured directly but the friction on the rail surface can be measured using measurement techniques. Estimation of wheel-rail adhesion conditions during railway operations can characterize the braking and traction control system. This paper presents the particle swarm optimization (PSO) based Extended Kalman Filter (EKF) to estimate adhesion force. The main limitation in applying EKF to estimate states and parameters is that its optimality is critically dependent on the proper choice of the state and measurement noise covariance matrices. In order to overcome the mentioned difficulty, a new approach based on the use of the tuned EKF is proposed to estimate induction motor (as a main part of the train moving system) parameters. This approach consists of two steps: In the first step the covariance matrices are optimized by PSO and then, their values will be introduced in the estimation loop. .
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.
