Article
Automation & Control Systems
Omid Elhaki, Khoshnam Shojaei
Summary: This paper proposes a novel robust saturated actor-critic multi-layer neural network controller for electrically-driven tractors with n-trailer. It addresses the challenges of unmeasurable linear and angular velocities, uncertain complex dynamics, and actuator saturation while ensuring a prescribed performance. The controller consists of four control loops for tracking, model approximation, robust control, and actuator control.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Article
Automation & Control Systems
Omid Elhaki, Khoshnam Shojaei
Summary: This paper proposes a novel robust saturated actor-critic multi-layer neural network controller for electrically-driven tractors with n-trailer, which ensures prescribed performance and stability while addressing uncertainties, actuator saturation, and disturbances. The controller consists of four control loops, each addressing different aspects of the system dynamics to achieve reliable and effective control.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Omid Elhaki, Khoshnam Shojaei, Parisa Mehrmohammadi
Summary: This paper proposes a high-performance intelligent online adaptive robust saturated dynamic surface control framework for underactuated autonomous underwater vehicles by utilizing Actor-Critic neural networks to address unmodeled dynamics, uncertainties, ocean disturbances, and actuator saturation. The controller is designed based on a reinforcement learning method and the Actor-Critic neural networks are trained real-time to enhance performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Aerospace
Omid Elhaki, Khoshnam Shojaei
Summary: A novel model-free saturated prescribed performance reinforcement learning framework is proposed to improve the trajectory tracking performance for quadrotors with control input saturation in the presence of model uncertainties, nonlinearities, and external disturbances. The framework includes the use of saturation functions, intelligent compensation for actuator saturation nonlinearity, prescribed performance control, adaptive robust controllers, and actor-critic neural networks. The proposed method is computationally cost-effective and shows stable convergence behavior during online training, as verified through simulations and quantitative comparisons.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Marine
Zilong Song, Zheyuan Wu, Haocai Huang
Summary: This paper proposes a cooperative learning formation control method for parametric path tracking of multiple AUVs in the presence of uncertainties and external disturbances. The control law allows independent velocity specification while accurately tracking the path. The use of localized radial basis function neural networks facilitates cooperative learning of uncertainties and construction of an empirically based formation control law. A novel finite-time distributed observer is presented for estimating the leader's position, and a finite-time performance function is used to accelerate the learning process. Simulation results confirm the effectiveness of the proposed control protocol.
Article
Automation & Control Systems
Yuanbo Su, Hongjing Liang, Yingnan Pan, Duxin Chen
Summary: This paper addresses the trajectory tracking control problem of autonomous underwater vehicles with actuator faults and model uncertainties. A novel BLF-based funnel tracking control strategy and event-triggered adaptive fuzzy fault-tolerant control scheme are proposed to handle the uncertainties and reduce communication burden and excess wear. The effectiveness of the method is demonstrated through simulation examples.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Omid Elhaki, Khoshnam Shojaei, Ardashir Mohammadzadeh, Sakthivel Rathinasamy
Summary: This paper proposes a new observer-based bounded adaptive fuzzy controller for robotic manipulators with a prescribed performance subjected to uncertainties. Interval type-3 fuzzy logic systems are introduced, and the system uncertainties are modeled by an interval type-3 fuzzy neural network. The controller is designed based on a robust adaptive command-filtered backstepping control scheme, with projection-type adaptive laws and saturation functions utilized to ensure actuator limitations are not violated.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Marine
Caipeng Ma, Yu Tang, Ming Lei, Dapeng Jiang, Wanzhen Luo
Summary: This paper focuses on the 3D trajectory tracking control of a fully-actuated AUV in the presence of uncertainties, unmeasured velocity, external disturbance, and input saturation. It proposes an extended state observer (ESO) to estimate the unmeasured velocity and total disturbances, and a saturated controller based on contraction theory to ensure trajectory tracking and actuator limit avoidance.
Article
Engineering, Marine
Pham Nguyen Nhut Thanh, Ho Pham Huy Anh
Summary: This paper presents a study on the design of horizontal and depth controllers for autonomous underwater vehicles (AUVs) considering model uncertainties, input constraints, and ocean currents. The AUV's depth-plane and horizontal-plane models are established comprehensively, taking into account the ocean currents. Extended perturbation observers (EPOs) are constructed to estimate the ocean currents accurately in the presence of time-varying ocean currents. Neural network-based adaptive control is used to handle model uncertainties and environmental disturbances, and auxiliary systems with a smooth switching function are introduced to mitigate the effect of input saturation. Rigorous theoretical analyses demonstrate the robust stability of the proposed controllers, and extensive numerical simulation studies confirm their effectiveness, robustness, anti-jamming ability, and feasibility.
Article
Automation & Control Systems
Heng Wang, Tengfei Zhang, Xiaoyu Zhang, Qing Li
Summary: This paper investigates the problem of path tracking control for Autonomous Ground Vehicles (AGVs), considering input saturation, system nonlinearities, and uncertainties. Firstly, a linear parameter varying (LPV) model is formulated for the nonlinear path tracking system, taking into account the variation of vehicle velocity. Secondly, an observer-based control strategy is proposed to mitigate the effects of noise on lateral offset and heading angle measurements, using a finite frequency H-infinity index to tackle the derivative of desired heading angle's impact on path tracking error. Thirdly, sufficient conditions are derived to guarantee robust H-infinity performance of the path tracking system, with the calculation of observer and controller gains converted into a convex optimization problem. Finally, simulation examples verify the advantages of the proposed control method in this study.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Automation & Control Systems
Yingjiang Zhou, Cheng Li, Guo-Ping Jiang, Jingyu Liu
Summary: This paper addresses the prescribed-time leader-following consensus problem of multi-agent systems with actuator saturation. A protocol is proposed to assign convergence time offline and solve the actuator saturation problem. A disturbance and actuator faults observer is designed for estimating the disturbance and actuator faults, with numerical simulations demonstrating the efficiency of the observer and controller.
ASIAN JOURNAL OF CONTROL
(2022)
Article
Computer Science, Information Systems
Lubna Khasawneh, Manohar Das
Summary: This paper addresses the challenges associated with steering-angle control of electric power steering for autonomous vehicles. It proposes a variable gain-sliding mode steering-angle controller and develops a sliding mode observer to estimate the self-aligning moment disturbance and other disturbances. Simulation and experimental results demonstrate the stability and robustness of the proposed methods to the challenges mentioned.
Article
Engineering, Electrical & Electronic
Guoshun Cai, Liwei Xu, Ying Liu, Jiwei Feng, Jinhao Liang, Yanbo Lu, Guodong Yin
Summary: This paper addresses the uncertainties and disturbances in the path tracking control of autonomous vehicles (AVs) by using preview control theory. It considers unmeasureable multi-uncertainties and external disturbance, handles time-varying system delays, and considers physical constraints for practical implementation. The proposed controller design is formulated as an optimization problem based on linear matrix inequalities (LMIs) using convexification techniques.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Sauranil Debarshi, Suresh Sundaram, Narasimhan Sundararajan
Summary: This paper presents a coupled, neural network-aided controller for autonomous vehicles with model uncertainties and unknown external disturbances. The proposed controller utilizes an adaptive neural network for learning vehicle dynamics and incorporates a self-regulating learning scheme for better generalization performance. Simulation results demonstrate the effectiveness of the proposed control scheme in achieving better tracking performance in unknown environments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Marine
Jaime Arcos-Legarda, Alvaro Gutierrez
Summary: This work aims to develop a robust model predictive control (MPC) based on the active disturbance rejection control (ADRC) approach by using a discrete extended disturbance observer (ESO). The proposed technique combines disturbances and uncertainties into a total disturbance using the ADRC approach, which is estimated and rejected through feedback control using a discrete ESO. The performance of the proposed control technique is evaluated through simulation in a robotic autonomous underwater vehicle (AUV), showing superiority over classical MPC in reference tracking, external disturbances rejection, and model uncertainties attenuation tests.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Marine
Omid Elhaki, Khoshnam Shojaei
Article
Automation & Control Systems
Omid Elhaki, Khoshnam Shojaei
IET CONTROL THEORY AND APPLICATIONS
(2020)
Article
Automation & Control Systems
Omid Elhaki, Khoshnam Shojaei
Summary: This paper investigates a nonlinear platoon controller design for a group of autonomous tractor-trailers with limited communication ranges, aiming to increase load transportation capacity. By utilizing a prescribed performance strategy, the controller effectively achieves consecutive tractor-trailers to follow each other in a convoy-like formation within the group, ensuring connectivity and avoiding collisions.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Acoustics
Omid Elhaki, Khoshnam Shojaei
Summary: This study proposes a method for platoon formation control of a team of autonomous car-like mobile robots using a platoon controller and high-gain observer, with dynamic surface control method to prevent complexity. The tracking performance is improved by using adaptive neural networks and robust controllers.
JOURNAL OF VIBRATION AND CONTROL
(2022)
Article
Engineering, Marine
Omid Elhaki, Khoshnam Shojaei
Summary: A saturated trajectory tracking controller is proposed for high-speed surface vessels in this paper, utilizing a stabilizing hyperbolic tangent function and a dynamic surface control approach to enhance performance in the presence of various disturbances. The novel controller combines adaptive neural networks and adaptive robust controllers to compensate for unknown dynamics and disturbances, as demonstrated by simulations verifying the efficacy of the proposed control scheme.
Article
Engineering, Aerospace
Omid Elhaki, Khoshnam Shojaei
Summary: A novel model-free saturated prescribed performance reinforcement learning framework is proposed to improve the trajectory tracking performance for quadrotors with control input saturation in the presence of model uncertainties, nonlinearities, and external disturbances. The framework includes the use of saturation functions, intelligent compensation for actuator saturation nonlinearity, prescribed performance control, adaptive robust controllers, and actor-critic neural networks. The proposed method is computationally cost-effective and shows stable convergence behavior during online training, as verified through simulations and quantitative comparisons.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Omid Elhaki, Khoshnam Shojaei, Parisa Mehrmohammadi
Summary: This paper proposes a high-performance intelligent online adaptive robust saturated dynamic surface control framework for underactuated autonomous underwater vehicles by utilizing Actor-Critic neural networks to address unmodeled dynamics, uncertainties, ocean disturbances, and actuator saturation. The controller is designed based on a reinforcement learning method and the Actor-Critic neural networks are trained real-time to enhance performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Omid Elhaki, Khoshnam Shojaei, Declan Shanahan, Allahyar Montazeri
Summary: In this paper, a new neuro-fuzzy reinforcement learning-based control structure is proposed for precise trajectory tracking of autonomous underwater vehicles. By integrating multiple neural networks and fuzzy neural networks with a high-gain observer, a robust smart observer system is established to accurately estimate the velocities and dynamic parameters of AUVs.
Proceedings Paper
Engineering, Electrical & Electronic
Omid Elhaki, Khoshnam Shojaei
2018 6TH RSI INTERNATIONAL CONFERENCE ON ROBOTICS AND MECHATRONICS (ICROM 2018)
(2018)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
