Article
Automation & Control Systems
Wei Xue, Xiaoli Luan, Shunyi Zhao, Fei Liu
Summary: In this paper, the Kalman filter and unbiased finite impulse response filter are fused to improve robustness against uncertainties. The proposed influence finite impulse response filter does not require noise statistics and shows adaptive performance in switching between estimates based on operating conditions. It provides state estimates of best accuracy among all the compared methods.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Automation & Control Systems
Shunyi Zhao, Yuriy S. Shmaliy, Choon Ki Ahn, Fei Liu
Summary: The UFIR filtering algorithm designed for industrial processes with unknown measurement data covariance estimates data noise covariance recursively using the VB approach, and optimizes the averaging horizon length in real time. Applied to specific systems, the VB-UFIR algorithm self-estimates N-opt more accurately than known solutions, producing stable and reliable estimates without divergence compared to the VB-Kalman filter.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Yuriy S. Shmaliy
Summary: This paper develops a novel theory of robust a posteriori H-2 optimal finite impulse response (OFIR) filtering for uncertain and disturbed systems. The H-2-OFIR filter is derived by minimizing the squared Frobenius norm of the weighted error-to-error transfer function using the backward Euler method. Simulation and experimental examples demonstrate that the H-2-OFIR filter outperforms other filters in terms of robustness.
IEEE SYSTEMS JOURNAL
(2023)
Article
Automation & Control Systems
Sung Hyun You, Choon Ki Ahn, Shunyi Zhao, Yuriy S. Shmaliy
Summary: This article presents a new approach to designing the Frobenius norm-based weighted unbiased FIR fusion filter, which demonstrates higher robustness in wireless sensor networks.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Karen J. Uribe-Murcia, Yuriy S. Shmaliy
Summary: The study presents the development of a robust unbiased finite impulse response (UFIR) filter for wireless sensor networks with multistep random delays and multiple dropouts. Experimental testing with randomly delayed data demonstrated that the UFIR filter is the most robust and accurate filter when uncertainties and delay probabilities are appropriately addressed.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Shunyi Zhao, Yuriy S. Shmaliy, Jose A. Andrade-Lucio, Fei Liu
Summary: This article develops a multipass optimal finite impulse response (OFIR) filtering approach for industrial processes with unknown initial conditions and temporary model mismatches. The new double-pass OFIR (DOFIR) and triple-pass OFIR (TOFIR) filters are shown to significantly improve performance close to initial values and have higher robustness compared to existing filtering methods like Kalman, OFIR, and UFIR filters.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Karen Uribe-Murcia, Oscar G. Ibarra-Manzano, Jose A. Andrade-Lucio, Yuriy S. Shmaliy
Summary: In this study, the UFIR filter, KF, and game theory H-8 filter are proposed for vehicle tracking in WSNs with multi-step random delays and multiple dropouts. The problem of data delays is solved by converting a model with delays to another without delays. The dropouts are detected and compensated for by prediction. Experimental tests using a vehicle tracking database show that the UFIR filter is the most robust, KF is the most accurate when delay probabilities are known, and the game theory H-8 filter is the most accurate under ideal conditions but prone to divergency otherwise.
Article
Automation & Control Systems
Ran Yan, Junliang Liu, Jianfeng Wu, Yonghui Hu
Summary: An extended measurement model anomaly detection algorithm for atomic clocks is proposed based on the p-step Kalman-like iterative UFIR algorithm, which accumulates prediction residuals to construct detection statistics for the detection of weaker phase and frequency jumps of atomic clocks.
MEASUREMENT & CONTROL
(2023)
Article
Automation & Control Systems
Juan J. Lopez-Solorzano, Yuriy S. Shmaliy
Summary: This article presents a discrete convolution-based H infinity (H∞)-FIR observer for disturbed systems under measurement and initial errors. The gain for the H∞-FIR observer is obtained by solving a linear matrix inequality (LMI) numerically. By introducing an additional variable and proving a theorem, the LMI is modified and constrained to include a quadratic term with respect to the filter gain. Numerical and experimental results demonstrate that the developed H∞-FIR observer outperforms optimal FIR and Kalman filters in accuracy for disturbed systems operating under measurement and initial errors, while maintaining similar robustness to a robust unbiased FIR filter.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Stefan Wernitz, Eleni Chatzi, Benedikt Hofmeister, Marlene Wolniak, Wanzhou Shen, Raimund Rolfes
Summary: In this study, we introduced a method for noise covariance estimation and damage analysis in structural health monitoring using Kalman filters. By utilizing an autocovariance least-squares method based on model parameters, we simplified the tuning of the filters. Additionally, we proposed a new damage indicator that exhibits high sensitivity towards localized damage. Through simulation and experimental studies, we demonstrated the effectiveness of the proposed methods and found that the combined application of different filters can enhance the robustness and sensitivity of damage detection and localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Yanda Guo, Xuyou Li, Qingwen Meng
Summary: A novel robust MCFIR filter was proposed to deal with the state estimation problem in the linear state-space system corrupted by outliers. Gaussian correntropy was used to improve the filter's robustness to outlier interference. An unbiased MCFIR filter was derived that ignores noise statistics, and an improvement bias-constrained MCFIR filter was proposed to achieve better estimate accuracy.
Article
Automation & Control Systems
Elham Javanfar, Mehdi Rahmani
Summary: This paper investigates the Lp filtering problem for linear dynamic systems, focusing on discussing the optimal filter for non-Gaussian systems. The obtained linear filter structure and proposed algorithms are shown to have superior performance in simulation experiments for different types of systems.
IET CONTROL THEORY AND APPLICATIONS
(2021)
Article
Automation & Control Systems
Yuriy S. Shmaliy, Oscar G. Ibarra Manzano, Jose A. Andrade Lucio
Summary: In this article, a robust H-2 optimal unbiased FIR predictor and filter are developed for uncertain and disturbed systems, demonstrating superior performance over existing filters under severe disturbances and large timing error.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Environmental Sciences
Zihao Huang, Shijin Chen, Chengpeng Hao, Danilo Orlando
Summary: In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) is popular for stability and low computational burden, but suffers from bias problems due to correlated measurement vector and noise; an unbiased PLKF algorithm (UB-PLKF) is proposed to address this issue, along with a velocity-constrained version (VC-PLKF) to further improve performance, outperforming other methods in both non-manoeuvring and manoeuvring scenarios according to simulations.
Article
Computer Science, Information Systems
Eli G. Pale-Ramon, Luis J. Morales-Mendoza, Mario Gonzalez-Lee, Oscar G. Ibarra-Manzano, Jorge A. Ortega-Contreras, Yuriy S. Shmaliy
Summary: The paper addresses the challenge of accurately estimating object trajectory in the bounding box of a video camera under environmental disturbances in visual object tracking. It considers bounding box variations as Gaussian-Markov colored measurement noise and proposes a robust unbiased finite impulse response filter as well as a general Kalman filter as a benchmark. The Car4benchmark is used to test the algorithms, and the results show that the robust GUFIR filter achieves the best tracking performance under heavy disturbances.
Article
Computer Science, Artificial Intelligence
Chun Xin, Yuan-Xin Li, Choon Ki Ahn
Summary: This paper proposes a novel command filtered backstepping adaptive controller to address the adaptive neural asymptotic tracking issue for uncertain non-strict feedback systems subject to full-state constraints. The control scheme not only deals with full-state constraints effectively but also avoids the "explosion of complexity" issue. The stability analysis proves that the tracking error asymptotically converges to zero, all the variables in the controlled systems are bounded, and all the states are constrained.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiongliang Zhang, Shiqi Zheng, Choon Ki Ahn, Yuanlong Xie
Summary: This article investigates consensus control for a class of fractional-order nonlinear multi-agent systems (MASs) considering severe sensor/actuator faults and time-varying delays. A new adaptive controller, composed of distributed FO Nussbaum gain, FO filter, and auxiliary function, is proposed to handle severe faults. Two different methods based on barrier Lyapunov function and Lyapunov-Krasovskii function are proposed to deal with time-varying delays. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate unknown nonlinear functions, resulting in a low-complexity controller. Two simulation examples are used to verify the validity of the proposed schemes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Dong Kyu Lee, Hyun Ho Kang, Jung Min Pak, Choon Ki Ahn
Summary: This paper proposes a continuous-time optimal unbiased FIR filter for input-delayed systems. By introducing a new integral transformation relation, the filter problem is represented as an optimal control problem, and the filter gain function is obtained by solving differential equations. The main advantage of this solution is that it provides maximum likelihood estimation without requiring initial values.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Siwen Hao, Yingnan Pan, Yanzheng Zhu, Choon Ki Ahn
Summary: This article proposes a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems with partially unmeasurable states and time-varying output constraints. Radial basis function neural networks are used to model unknown functions, and a reduced-order observer is employed to estimate the partially unmeasurable states. By constructing a transferred function, the system outputs are constrained within a time-varying constraint bound. The proposed approach also reduces computational burden by using first-order sliding mode differentiators. The decentralized adaptive fixed-time controllers are constructed based on Lyapunov function and fixed-time theory, achieving fixed-time stability and restricting output signals within a bounded compact set. The effectiveness of the proposed control scheme is demonstrated through simulation examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Mechanical
Yongliang Yang, Liqiang Tang, Wencheng Zou, Choon Ki Ahn
Summary: This paper presents a novel command-filtered Nussbaum design scheme that can handle multiple unknown high-frequency gains using compensating signals and additional adaptive laws. The effect of filtering errors on tracking performance is analyzed within the Lyapunov stability framework.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Kuo Li, Changchun Hua, Xiu You, Choon Ki Ahn
Summary: This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. A novel consensus scheme is proposed with a simple design procedure, and its feasibility is checked via numerical simulation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Kamil Hassan, Fatima Tahir, Muhammad Rehan, Choon Ki Ahn, Mohammed Chadli
Summary: This article addresses the group consensus problem in a network of multiagent systems by proposing a relative-output-based distributed control law. By utilizing Lyapunov stability theory and a linear matrix inequality, the sufficient and necessary conditions for achieving group consensus are formulated and validated.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Guangliang Liu, Hongjing Liang, Yingnan Pan, Choon Ki Ahn
Summary: This article investigates the problem of bipartite consensus tracking control for nonlinear networked systems. It proposes an adaptive control protocol based on neural networks and backstepping technology, which ensures the boundedness of signals in the closed-loop system and achieves bipartite consensus control.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
He Liu, Xiao-Jian Li, Chao Deng, Choon Ki Ahn
Summary: This study investigates the problem of fault estimation and control for unknown discrete-time systems. A data-driven parameterization controller design method is proposed to optimize both fault estimation and robust control performances.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xiao-Zhen Liu, Kai-Ning Wu, Choon Ki Ahn
Summary: This article studies the synchronization problem of coupled fractional delayed reaction-diffusion neural networks with boundary controllers. The study presents both time-continuous and time-discontinuous controllers and analyzes the effects of control parameters on system performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Liheng Chen, Yanzheng Zhu, Choon Ki Ahn
Summary: This study proposes a novel approach for adaptive neural network observer design in continuous-time switched systems using quantized output signals. The approach combines adaptive laws and persistent dwell time switching to accurately estimate the system state and actuator efficiency factor.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yajing Yu, Jian Guo, Choon Ki Ahn, Zhengrong Xiang
Summary: This paper investigates the problem of neural adaptive distributed formation control for quadrotor multiple UAVs subject to unmodeled dynamics and disturbance. By dividing the quadrotor UAV system into position and attitude subsystems, a virtual position controller and a neural adaptive sliding mode controller are designed to achieve formation flight for multi-UAVs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Zhenghong Jin, Xingjian Sun, Zhengyan Qin, Choon Ki Ahn
Summary: This paper studies the distributed optimization problem of Takagi-Sugeno fuzzy cyber-physical systems using weight-balanced graphs and quasistrongly connected characteristics. The objective is to drive the outputs of all agents to the optimal solution of a given global objective function based on partial information of the local objective functions. Distributed optimal coordinators and fuzzy reference-tracking controllers are used to achieve this goal. The paper proposes two Lyapunov-based fuzzy input-to-state stability small-gain theorems for the T-S fuzzy interconnected system.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yue-Yue Tao, Zheng-Guang Wu, Tingwen Huang, Prasun Chakrabarti, Choon Ki Ahn
Summary: This study focuses on the design problem of asynchronous event-triggered output-feedback controller for discrete-time singular Markov jump systems. A hidden Markov model is used to estimate the system mode, and an output-feedback control scheme and HMM-based event-triggered mechanism are employed to reduce the communication burden. Sufficient conditions for the stochastic admissibility of the closed-loop system with a prescribed H-infinity performance index are established using the Lyapunov functional technique, and the design procedures are summarized as an optimization algorithm based on linear matrix inequalities.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Guohuai Lin, Hongyi Li, Choon Ki Ahn, Deyin Yao
Summary: This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It addresses the issues of external disturbances and uncertain nonlinear dynamics using a disturbance observer and radial basis function neural networks (RBF NNs). The proposed finite-time command filtered (FTCF) backstepping method effectively manages the complexity explosion problem and an event-triggered mechanism is considered to alleviate the communication burden.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
