Article
Automation & Control Systems
Daniele Astolfi, Swann Marx, Nathan van de Wouw
Summary: A new design for repetitive control scheme for nonlinear minimum-phase systems is proposed, utilizing a forwarding-based state-feedback design for handling delays. The theoretical analysis demonstrates that the proposed control design can achieve asymptotic convergence of the desired regulated output.
Article
Automation & Control Systems
Li Li, Hongyang Zhao, Fazhi Song
Summary: This paper investigates the projection-based iterative learning control (P-ILC) scheme in lithographic machine tools, and enhances its performance by introducing a set-membership based frequency-domain ILC algorithm (SM-F-ILC). The results demonstrate that SM-F-ILC can compensate for errors, attenuate error accumulation, and achieve fast convergence even with model uncertainties.
Article
Computer Science, Information Systems
Shuai Zhang, Jiaxi Chen, Chan Bai, Junmin Li
Summary: This paper proposes a new global fuzzy iterative learning scheme for nonlinear multi-agent systems with unknown dynamics. Unlike traditional design schemes, where fuzzy systems are used as feedback compensators, this scheme utilizes fuzzy systems as feedforward compensators to describe the unknown dynamics, thus avoiding restrictions on the control system states. In this scheme, a hybrid fuzzy adaptive learning controller is designed based on the characteristics of the network structure. The effectiveness of this hybrid learning protocol is verified through simulations.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Yuxin Wu, Deyuan Meng, Jinrong Wang, Kaiquan Cai
Summary: This paper proposes a distributed learning algorithm for iterative learning control (ILC) systems, aiming to achieve perfect tracking tasks. By integrating observer-based design and consensus-based design ideas, a multiagent system is constructed to solve linear algebraic equations with partial information. With this algorithm, all agents can agree on a common solution for any solvable linear algebraic equations under any initial conditions of agents, regardless of whether it has a unique solution or not.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hong-Gui Han, Chen-Yang Wang, Hao-Yuan Sun, Hong-Yan Yang, Jun-Fei Qiao
Summary: In this article, a fuzzy neural network-based iterative learning model predictive control (FNN-ILMPC) is designed for complex nonlinear systems. The controller considers the impact of external disturbances, effectively eliminates their influence, and ensures system stability.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Mathematics
Qing-Yuan Xu, Wan-Ying He, Chuang-Tao Zheng, Peng Xu, Yun-Shan Wei, Kai Wan
Summary: An adaptive fuzzy iterative learning control algorithm is proposed for the iterative variable reference trajectory problem in nonlinear discrete-time systems with input saturations and unknown control directions. The algorithm combines a fuzzy logic system to compensate for input saturation and adopts the discrete Nussbaum gain technique to identify the control direction of the system. The convergence and boundedness of the system signals are proven based on a nonincreasing Lyapunov-like function. Simulation results demonstrate the feasibility and effectiveness of the learning control method.
Article
Computer Science, Information Systems
Zheng Hong, Qiuzhen Yan, Xiushan Wu, Jianping Cai
Summary: This paper proposes a fuzzy system-based barrier adaptive iterative learning control scheme for accurate position tracking and effective system constraint in tank gun control systems. The initial position problem is solved using an error tracking strategy, and a barrier Lyapunov function is adopted to design the controller for system constraint. A fuzzy system is used as an approximator to compensate for nonparametric uncertainties, and a difference learning approach is employed to estimate the optimal parameters of the fuzzy systems. Experimental results demonstrate the effectiveness of the proposed method.
Article
Engineering, Chemical
Chiang-Ju Chien, Ying-Chung Wang
Summary: In this paper, an iterative learning control problem for a class of unknown discrete-time nonlinear systems is considered. The uncertainties, including iteration-varying initial error, system parameters, external disturbance, desired output, and control direction, can be overcome by proposing an iterative learning control law with an adaptive iteration-varying fuzzy system. A sufficient condition for designing adaptive gains is presented, and the convergence of the learning error to a small value as the trial number is large enough is proven. Two simulation examples are used to demonstrate the theoretical results.
Article
Automation & Control Systems
Deyuan Meng
Summary: This paper aims to solve the control analysis and synthesis problem of data-driven learning when plant models are unknown and uncertainties vary during iterations. A Kalman state-space approach is proposed to transform the target tracking into two robust stability problems, connecting data-driven control and model-based control. The extended state observer (ESO) is employed to deal with iteration-varying uncertainties in the design of data-driven learning. The results show that ESO-based data-driven learning ensures robust tracking of desired targets for model-free systems, especially under quasi-disappearing variation of uncertainties. The applicability of the proposed approach to iterative learning control is also demonstrated through an example.
Article
Acoustics
Jing Huang, Huayi Zheng, Hong Li, Guoxiu Li, Cheng Qiu
Summary: The article introduces an improved control algorithm that addresses the error divergence issue of the system under phase delay and mathematically proves its convergence. Experimental verification shows that the new algorithm has a better control effect than the traditional one.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Mathematics, Interdisciplinary Applications
Xiulan Zhang, Ming Lin, Fangqi Chen
Summary: This paper investigates the adaptive fuzzy backstepping control for a specific category of incommensurate fractional-order chaotic systems affected by functional uncertainties and actuator faults. The proposed approach utilizes a modified fractional-order robust differentiator to address the complexity issue and a novel iterative learning adaptation law to improve the approximation accuracy of fuzzy logic systems (FLSs). The stability and convergence of the closed-loop system are guaranteed by the frequency distribution model and the Lyapunov stability criterion.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Qiongxia Yu, Zhongsheng Hou
Summary: The article proposes a new adaptive fuzzy iterative learning control (AFILC) method for tracking control of high-speed trains (HST) that have uncertain nonlinear operation systems. The AFILC method can actively manipulate position, speed, and input force of the train to ensure safe operation and minimize tracking control errors over varying time intervals. Simulations on a practical train operation system similar to China Railway High-speed (CRH)-3 train demonstrate the applicability and effectiveness of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Jingyao Zhang, Deyuan Meng
Summary: This paper proposes a robust tracking method based on an iterative rectifying mechanism for continuous-time ILC systems subject to nonrepetitive uncertainties. By combining a contraction mapping-based method and a system equivalence transformation method, a robust convergence analysis for continuous-time ILC is achieved. The results show that robust tracking tasks can be accomplished in the presence of nonrepetitive uncertainties, regardless of the system relative degrees being zero or nonzero.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Miel Sharf, Anne Koch, Daniel Zelazo, Frank Allgoewer
Summary: In this article, we develop a data-based controller design framework for diffusively coupled systems, ensuring convergence to an F-neighborhood of the desired formation. The controller consists of a fixed controller with adjustable gain on each edge. By utilizing passivity theory and network optimization, we not only prove the existence of a gain that achieves the desired formation control goal, but also present a data-based method to determine an upper bound for this gain.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Song Lu, Shi Jingzhuo
Summary: In this study, a new online adaptive adjustment method for PI control parameters is proposed based on the concept of iterative learning control (ILC). By utilizing simple P-type and PD-type iterative learning controllers, the proportional and integral coefficients of the PI controller are adaptively adjusted to ensure the desired control performance. Comparative experiments demonstrate the effectiveness of the proposed control strategy.
Article
Automation & Control Systems
Radu-Emil Precup, Radu-Codrut David, Raul-Cristian Roman, Alexandra-Iulia Szedlak-Stinean, Emil M. Petriu
Summary: This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. The newly developed version of the algorithm, SMAF1, shows superiority over other metaheuristic algorithms for the position control of nonlinear servo systems.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Theory & Methods
Marius Brezovan, Radu-Emil Precup, Dan Selisteanu, Liana Stanescu
Summary: An approach to CPN-based control is proposed in this paper, which allows modeling of both the controller and the controlled process in the control system structure. The controlled process is discretized in order to use CPN for modeling. The proposed controller, based on a Moore automaton, is validated through simulations and experimental results.
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
(2023)
Article
Automation & Control Systems
Raul-Cristian Roman, Radu-Emil Precup, Emil M. Petriu, Mihai Muntyan
Summary: The purpose of this paper is to propose a novel controller, called the FRIT-iPID controller, which combines the Fictitious Reference Iterative Tuning (FRIT) algorithm and the Model-Free Adaptive Control (MFC) algorithm in the context of the intelligent proportional-integral-derivative (iPID) controller. The FRIT algorithm optimally tunes the parameters of the iPID controller using the African Vultures Optimization Algorithm (AVOA). The FRIT-iPID controller is experimentally validated on a three-degree-of-freedom tower crane system for position control.
STUDIES IN INFORMATICS AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Awudu Atinga, Jozsef K. Tar
Summary: The Fixed Point Iteration-based Adaptive Control design methodology is an alternative to the Lyapunov function-based technology. It contains higher-order feedback terms than the standard resolved acceleration rate control. This design approach strictly separates the kinematic and dynamic issues.
Editorial Material
Automation & Control Systems
Anh-Tu Nguyen, Truong Quang Dinh, Junjie Chong, Makoto Iwasaki, Radu-Emil Precup, Michael Ruderman
CONTROL ENGINEERING PRACTICE
(2023)
Article
Computer Science, Artificial Intelligence
Iuliu Alexandru Zamfirache, Radu-Emil Precup, Raul-Cristian Roman, Emil M. Petriu
Summary: This paper introduces a novel reference tracking control approach using a combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf Optimizer (GWO) algorithm. The GWO algorithm replaces the traditional neural network-based implementation of the Critic to overcome the drawbacks of slow convergence and tendency to get stuck in local optimal values. The proposed approach, with the GWO-based critic, demonstrates superior performance in solving the optimal reference tracking control problem on nonlinear servo system laboratory equipment compared to other control approaches using traditional optimization techniques or metaheuristic algorithms like Particle Swarm Optimization (PSO).
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Gheorghe Duca, Sergey Travin, Inga Zinicovscaia, Radu-Emil Precup
Summary: This paper presents an approach to describe biomonitoring data using mosses and focuses on the case of Republic of Moldova. The elemental composition of 33 moss samples collected in Moldova was analyzed using neutron activation analysis and atomic absorption spectrometry. The data was then analyzed and sorted using correlation analysis and smoothing techniques. An algorithm for determining the number of linearly independent vectors in the matrix was also developed.
ROMANIAN JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY
(2023)
Proceedings Paper
Automation & Control Systems
Raul-Cristian Roman, Radu-Emil Precup, Emil M. Petriu, Mihai Muntyan, Elena-Lorena Hedrea
Summary: This paper introduces a hybrid data-driven control algorithm obtained by combining two data-driven algorithms, namely intelligent proportional-integral controllers as representatives of Model-Free Control (MFC) algorithms. The parameters of the algorithm are optimally tuned using the Fictitious Reference Iterative Tuning (FRIT) algorithm with the metaheuristic Slime Mould Algorithm. Real-time experiments on a 3 degrees of freedom tower crane system equipment are conducted to demonstrate the efficiency of the novel data-driven algorithms.
2023 31ST MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION, MED
(2023)
Proceedings Paper
Automation & Control Systems
Claudia-Adina Bojan-Dragos, Radu-Emil Precup, Alexandra-Iulia Szedlak-Stinean, Raul-Cristian Roman, Elena-Lorena Hedrea, Emil M. Petriu
Summary: This paper aims to design optimal sliding mode and super twisting sliding mode controllers for nonlinear Shape Memory Alloy (SMA) wire actuators. The parameters of the proposed controllers are optimally tuned using the metaheuristic Grey Wolf Optimizer algorithm and a comparative analysis is carried out. All control structures are validated by simulations using an accurate evolved Takagi-Sugeno-Kang fuzzy model of SMA wire actuators, and their control performance is evaluated.
2023 EUROPEAN CONTROL CONFERENCE, ECC
(2023)
Article
Engineering, Multidisciplinary
Claudiu Pozna, Radu-Emil Precup
Summary: This paper introduces a new approach to geometric modeling that uses generalized coordinates and parameters to calculate the configuration of a structure. It replaces homogeneous transformations with quaternions, simplifying the modeling process and improving computational efficiency.
ACTA POLYTECHNICA HUNGARICA
(2023)
Article
Engineering, Mechanical
Anamaria-Ioana Borlea, Radu-Emil Precup, Raul-Cristian Roman
Summary: This paper applies three classical and popular discrete-time model-based sliding mode controllers to the position control of tower crane systems. Separate single input-single output controllers are designed for cart position control, arm angular position control, and payload position control. Experimental results are included for comparison of the three sliding mode controllers.
FACTA UNIVERSITATIS-SERIES MECHANICAL ENGINEERING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Alexandra-Iulia Szedlak-Stinean, Radu-Emil Precup, Raul-Cristian Roman, Emil M. Petriu, Claudia-Adina Bojan-Dragos, Elena-Lorena Hedrea
Summary: This paper proposes four estimation techniques for a mechatronics system with state feedback control. The performance and effectiveness of these techniques in position control are validated through experiments and simulations.
2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
(2022)
Proceedings Paper
Engineering, Industrial
Radu-Emil Precup, Elena-Lorena Hedrea, Raul-Cristian Roman, Emil M. Petriu, Claudia-Adina Bojan-Dragos, Alexandra-Iulia Szedlak-Stinean
Summary: This paper suggests performance improvement of three Single Input-Single Output (SISO) fuzzy control systems for cart, arm angular, and payload positions of tower crane systems. It implements cost-effective S.S. fuzzy controllers as first-order discrete-time intelligent Proportional-Integral controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. The parameters of PD-type learning rules are optimally tuned using a metaheuristic Grey Wolf Optimizer algorithm.
2022 IEEE 31ST INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE)
(2022)
Article
Engineering, Multidisciplinary
Ferenc Tolner, Balazs Barta, Marta Takacs, Gyorgy Eigner
Summary: This study investigates different organizational types in Central Europe through online surveys and textual data analysis. The methods of clustering analysis and Latent Dirichlet Allocation are utilized to explore techniques for collaboration and business organization grouping, aiming to enhance the efficiency and sustainability of business network formations.
ACTA POLYTECHNICA HUNGARICA
(2022)
Article
Engineering, Multidisciplinary
Jelena Tasic, Marta Takacs, Levente Kovacs
Summary: This article reviews recently proposed advanced methods for controlling blood glucose levels in patients with type 1 diabetes. The artificial pancreas appears to have better control than conservative insulin administration, while also avoiding the risk of hypoglycemia or hyperglycemia. The commonly used methods are yielding positive results, and machine learning algorithms show promise. However, there are challenges in designing algorithms for the artificial pancreas.
ACTA POLYTECHNICA HUNGARICA
(2022)
