Article
Computer Science, Information Systems
Sultan Noman Qasem, Ali Ahmadian, Ardashir Mohammadzadeh, Sakthivel Rathinasamy, Bahareh Pahlevanzadeh
Summary: In this study, a new self-organizing interval type-3 fuzzy logic system is proposed with an adaptive fuzzy kernel size to enhance the robustness against non-Gaussian noise. Simulation examples demonstrate that the introduced system and learning algorithm outperform other types of fuzzy neural networks and conventional learning techniques in terms of accuracy. The proposed learning method shows improved robustness against non-Gaussian noise compared to traditional Kalman filters.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Haiquan Zhao, Boyu Tian, Badong Chen
Summary: In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods, achieving more accurate results.
Article
Engineering, Multidisciplinary
Zheng Liu, Min Zhang, Xinmin Song, Xuehua Yan
Summary: In this study, a fusion maximum correntropy Kalman/UFIR filter is proposed to achieve a more advantageous estimation effect by assigning specific probability weights to the two filters. The simulation results demonstrate the superiority of the presented fusion filter algorithm in comparison to the fusion Kalman/UFIR filter regarding the estimation performance under the non-Gaussian noise system.
Article
Computer Science, Artificial Intelligence
Amin Taghieh, Ardashir Mohammadzadeh, Chunwei Zhang, Nasreen Kausar, Oscar Castillo
Summary: This paper addresses the issues of current sharing and voltage balancing in direct current microgrids. A distributed control algorithm based on interval type-3 fuzzy logic system (IT3FLS) is proposed to overcome the challenges caused by unknown dynamic models and external disturbances. A learning strategy utilizing correntropy unscented Kalman filter (CUKF) with fuzzy kernel size is designed to improve the accuracy of approximation. The suggested control policy ensures convergence of the microgrid trajectories and robustness against uncertainties, as demonstrated by simulation results. The proposed controller shows promising performance for practical applications.
APPLIED SOFT COMPUTING
(2022)
Article
Thermodynamics
Wentao Ma, Peng Guo, Xiaofei Wang, Zhiyu Zhang, Siyuan Peng, Badong Chen
Summary: A robust CKF enhanced by the generalized maximum correntropy criterion (GMCC) is developed in this work, which can accurately estimate the SOC of lithium batteries under different operating conditions, especially in the presence of non-Gaussian noise, demonstrating its excellent performance.
Article
Computer Science, Artificial Intelligence
Zhao-Xu Yang, Hai-Jun Rong, Plamen Angelov, Zhi-Xin Yang
Summary: This article proposes a novel incremental statistical evolving fuzzy inference system (SEFIS) that can update system parameters and evolve structure components in the presence of non-Gaussian noises. The system generates new rules based on statistical model sufficiency and deletes inactive rules to improve performance and accuracy. Additionally, an adaptive maximum correntropy extend Kalman filter is introduced to update parameters and enhance robustness. Simulation studies demonstrate that the proposed SEFIS has faster learning speed and higher accuracy compared to existing evolving fuzzy systems (EFSs) in both noise-free and noisy conditions.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Ali Asghar Sheydaeian Arani, Mahdi Aliyari Shoorehdeli, Ali Moarefianpour, Mohammad Teshnehlab
Summary: This paper introduces a method for fault estimation in a nonlinear system using the unscented Kalman filter, augmented by a fault signal as a state variable. A filter combining Gaussian mixture model and augmented ensemble unscented Kalman filter is designed for estimating faults in nonlinear systems, with suitable conditions and assumptions for convergence. The proposed method is evaluated in simulating a bioreactor system, demonstrating better performance compared to traditional methods in the presence of non-Gaussian noise.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Tingli Su, Zidong Wang, Lei Zou, Fuad E. Alsaadi
Summary: This article investigates a Kalman filtering algorithm based on maximum correntropy for linear time-varying systems with non-Gaussian noises and randomly occurring uncertainties. The event-triggered mechanism is introduced to reduce unnecessary data transmission and communication resource consumption, and a novel performance index is proposed to reflect the joint effects from various factors.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Chemistry, Analytical
Dah-Jing Jwo, Yi-Ling Chen, Ta-Shun Cho, Amita Biswal
Summary: Multiple forms of interference and noise in GPS navigation can impact the preciseness of positioning and navigation. This paper proposes an adaptive filtering technique and a kernel bandwidth technique to address the issue of non-Gaussian and heavy-tailed noise. The use of a fixed adaptive kernel bandwidth improves the performance and robustness of traditional filtering algorithms.
Article
Energy & Fuels
Peng Guo, Wentao Ma, Dele Yi, Xinghua Liu, Xiaofei Wang, Lujuan Dang
Summary: This paper proposes a novel robust state estimation method that enhances the robustness of the square-root cubature Kalman filter by incorporating a mixture correntropy loss. The method overcomes the issue of non-Gaussian measurement noise interference and achieves high estimation accuracy in different cases.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Multidisciplinary
Dengliang Qi, Jingan Feng, Wenkang Wan, Bao Song
Summary: This article presents an estimation technique based on the maximum correntropy criterion combined with adaptive extended Kalman filter and extended Kalman filter. It aims to handle non-Gaussian noise in vehicle state estimation and shows stronger robustness and better accuracy.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Automation & Control Systems
Min Zhang, Wei Xing Zheng, Xinmin Song, Hongwei Yuan
Summary: This paper investigates the state estimation problem for a linear discrete-time system with both packet dropping and non-Gaussian noise. Maximum correntropy Kalman filter algorithms are derived under different packet dropping models. The paper discusses both cases of using only real-time known characteristic and also the statistical characteristic of the packet dropping variable for state estimation.
SYSTEMS & CONTROL LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Xiaoyu Fu, Xinmin Song
Summary: This study presents the design of a distributed Kalman filter that performs well in a sensor network with packet drops and non-Gaussian noise. The proposed filter combines the maximum correntropy criterion and state equality constraint information. It handles non-Gaussian noise using higher-order statistics and achieves superior estimation performance by incorporating state equality constraint information. The effectiveness of the filter is verified through simulation results.
Article
Automation & Control Systems
Jiacheng He, Gang Wang, Bei Peng, Qi Sun, Zhenyu Feng, Kun Zhang
Summary: This paper introduces the error entropy learning criterion widely used in information theoretic learning and the algorithms based on this criterion. To improve learning performance, the paper proposes using a mixture of two Gaussian functions as kernel functions and develops two new recursive least-squares algorithms based on this. The paper also explains how the mixture mechanism improves the performance of adaptive filtering algorithms and verifies the practicality of the proposed algorithms through simulation and application.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Aerospace
Gulay Unal
Summary: The paper proposes an integrated approach for aircraft fault-tolerant control, incorporating a Kalman filter, GOS for sensor fault isolation, and fuzzy logic for reconfiguration. This integrated approach is sensitive to faults with disturbances, and simulation results demonstrate its applicability to any linear system.
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY
(2021)
Article
Engineering, Aerospace
Jianghua Wang, Khalid A. Alattas, Yassine Bouteraa, Omid Mofid, Saleh Mobayen
Summary: An adaptive command-filtered backstepping sliding mode control scheme is proposed for finite-time tracking control of quad-rotor UAV system under modeling uncertainties and external disturbances.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Shu-Rong Yan, Wei Guo, Ardashir Mohammadzadeh, Sakthivel Rathinasamy
Summary: This study introduces a new control approach for active/reactive power control in modernized microgrids. The control method utilizes a fuzzy reference tracking linear quadratic regulator and an optimal H-infinity-based deep learned control to handle uncertainties and faults. The study presents several contributions and verifies the applicability of the suggested control method through simulations and real-time examination. A comparison with related controllers shows that the designed controller is more robust and accurate.
APPLIED INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Maryam Jafari, Saleh Mobayen, Farhad Bayat, Hubert Roth
Summary: The design of swing and stance control for path following of a prosthetic leg robot subject to uncertain dynamics and exterior disturbances is a serious issue. This paper proposes two novel finite-time controllers to address the control problems in the swing and stance phases, and the simulation results verify their effectiveness.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Automation & Control Systems
Omid Elhaki, Khoshnam Shojaei, Ardashir Mohammadzadeh
Summary: This paper presents a novel adaptive reinforcement learning control method with interval type-3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed controller is independent of system dynamics and only relies on measurable signals. An adaptive robust controller in collaboration with reinforcement learning significantly improves system robustness. Prescribed performance control methodology ensures predefined overshoot/undershoot, convergence rate, and final tracking accuracy. High-gain observer is employed to estimate quadrotor velocity and acceleration. Lyapunov-based stability analysis achieves uniform ultimate boundedness stability. The simulation section demonstrates better performance of the proposed intelligent controller with the learning algorithm compared to conventional techniques.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Kaushik Paul, Pampa Sinha, Yassine Bouteraa, Pawe Skruch, Saleh Mobayen
Summary: This research proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm to address the power system congestion cost problem. The algorithm determines the Generator Sensitivity Factors (GSF) to select influential power system generators for rescheduling their real power and reducing excess power flow. The IMRFO incorporates correction factors to improve coordination between exploration and exploitation phases. Experimental results validate the effectiveness of IMRFO in minimizing congestion cost and outperforming other optimization methods.
Article
Computer Science, Information Systems
Mateusz Orlowski, Pawel Skruch
Summary: This paper presents an approach for defining, solving, and implementing dynamic cooperative maneuver problems in autonomous driving applications. A reinforcement learning technique is applied to find a suboptimal policy. The trained policy has been successful in solving the cooperation problem in all scenarios and the positive effects of applying shared rewards between agents have been presented and studied. The results obtained in this work provide a window of opportunity for various automotive applications.
Article
Automation & Control Systems
Ardashir Mohammadzadeh, Hamid Taghavifar, Chunwei Zhang, Khalid A. Alattas, Jinping Liu, Mai The Vu
Summary: This study introduces a robust type-3 fuzzy controller implementation for the path-tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances. The proposed scheme is independent of the parameter information and assumes unknown and non-linear system dynamics. Control inputs are constructed to improve robustness and ensure stability by leveraging the Lyapunov stability theorem and Barbalat's lemma. Also, a predicate scheme based on non-linear predictive control technique is introduced to enhance the lateral displacement.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ommegolsoum Jafarzadeh, Seyyed Arash Mousavi Ghasemi, Seyed Mehdi Zahrai, Ardashir Mohammadzadeh, Ramin Vafaei Poursorkhabi
Summary: This paper introduces a novel adaptive neurochaotic fuzzy control system based on type-2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers. The proposed control system utilizes online estimation and adaptive parameter training methods to achieve efficient reduction of seismic responses such as maximum displacement and acceleration.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Haoping Wang, Omid Mofid, Saeed Amirkhani, Saleh Mobayen, Mai The Vu
Summary: In this paper, an adaptive concave barrier function scheme coupled with the non-singular terminal sliding mode control technique is proposed for finite-time tracking control of the under-actuated nonlinear system in the existence of model uncertainty, external disturbance, and input saturation. The proposed control method consists of several steps, including the design of a compensation system to overcome input saturation, the definition of tracking errors and sliding surface, the adoption of a prescribed performance control scheme, and the use of adaptive-based concave barrier function technique for model uncertainty and external disturbance compensation. The effectiveness of the proposed control method is demonstrated through simulations and experimental implementation on an air levitation system.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Mohsen Farbood, Zeinab Echreshavi, Mokhtar Shasadeghi, Saleh Mobayen
Summary: This paper proposes an event-triggered integral sliding mode control (ISMC) for perturbed nonlinear Takagi-Sugeno (TS) fuzzy systems. A disturbance observer is designed to estimate and reduce the unmatched disturbances. Two types of sliding surfaces are established to reduce computational burden and communication resources. The proposed control scheme ensures system performance enhancement and Zeno-free behavior.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Multidisciplinary Sciences
Farzad Soltanian, Mehdi Nosrati, Saleh Mobayen, Chuan-Chun Li, Telung Pan, Ming-Ta Ke, Pawel Skruch
Summary: This study investigates a high-figure of merit (FoM) plasmonic microwave resonator as a non-invasive sensor for monitoring the blood glucose variation rate in adults. Experimental results demonstrate the high precision and enhanced sensitivity of the proposed sensor.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Mohsen Farbood, Zeinab Echreshavi, Mokhtar Shasadeghi, Saleh Mobayen, Pawel Skruch
Summary: This paper introduces a new data-driven MPC structure based on two offline and online parts to achieve the robust and constrained performance in an optimal scheme. The first step is to design an offline data-driven controller based on the model matching condition for tracking performance. Additionally, a data-driven-based disturbance observer is presented to estimate the external disturbance in the offline procedure.
Article
Computer Science, Information Systems
Hamede Karami, Ngoc Phi Nguyen, Hamid Ghadiri, Saleh Mobayen, Farhad Bayat, Pawel Skruch, Fatemeh Mostafavi
Summary: This paper investigates the simultaneous design of a controller and Luenberger state observer for systems with various uncertainties. The state-feedback control and state-observer existence conditions are formulated using Linear Matrix Inequalities (LMIs). By defining the estimation error, the equations of the closed-loop system are rewritten. Simulation results on two examples demonstrate the effectiveness and reliability of the proposed approach.
Article
Engineering, Multidisciplinary
J. Mostafaee, S. Mobayen, B. Vaseghi, M. Vahedi
Summary: This study presents a new 5D nonlinear hyper-chaotic system and analyzes its standard behaviors. It also proposes a Fast Terminal Sliding Mode Control (FTSMC) scheme for the control and synchronization of the system. The new controller demonstrates high performance and finite-time stability. MATLAB simulations confirm the results.
Article
Computer Science, Information Systems
Yousef Niazi, Amirhossein Rajaei, Vahid Moradzadeh Tehrani, Mokhtar Shasadeghi, Saleh Mobayen, Pawel Skruch
Summary: This paper presents a voltage boost switched-capacitor multi-level inverter (SCMLI) structure based on Current-fed Dickson Voltage Multiplier (CFDVM) that allows control of voltage gain. The structure limits capacitor peak current and increases voltage gain compared to other SCMLIs, while high frequency operation reduces capacitor size. The SCMLI exhibits same output impedance at all voltage levels and utilizes Selective Harmonic Elimination (SHE) technique for improved output power quality.
