Iterative learning control for linear generalized distributed parameter system

Zhang, Yingjun; Li, Yinghui; Chen, Mengji

doi:10.1007/s00521-018-3835-0

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…As most fractional problems are difficult to obtain accurate solutions, more and more research is looking for their numerical algorithms. Many numerical algorithms solve fractional equations, such as the finite difference method [1]. As a typical fractional differential equation, the fractional reactiondiffusion equation has attracted everyone's attention.…”

Section: Introductionmentioning

confidence: 99%

The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping

Zhang

Yang

Arbab

2023

Applied Mathematics and Nonlinear Sciences

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This paper uses the principle of compressed mapping to discuss the existence and uniqueness of the explicit finite difference method for the fractional diffusion equation with time delay. The Laplace transform method obtains the necessary expression form of the solution. At the same time, the existence theorem and the existence and uniqueness theorem of the solution to the boundary value problem is established. Finally, an example is given to verify the correctness of the conclusion. The experimental results show that the parallel algorithm proposed in this paper agrees with the exact solution.

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Section: Introductionmentioning

confidence: 99%

The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping

Zhang

Yang

Arbab

2023

Applied Mathematics and Nonlinear Sciences

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“…The model can be extended to the singular distributed parameter system (SDPS). Compared with the generalized system and the distributed parameter system [28], the singular distributed parameter system is more generally. When subjected to external disturbance, the system will not only lose its stability, but also change its structure significantly [29].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Iterative learning control (ILC) is applied to a controlled system with repeated motion characteristics [28], and its goal is to complete the entire tracking task within a limited time interval [36]. It corrects the control signal according to the deviation between the output signal and the desired target, so as to improve the tracking performance of the system [37]. Iterative learning control is significant for nonlinear, strong coupling, difficult to model and repetitive motion of the controlled system [38], and it has been widely used in robot control, traffic flow control, industrial process, biomedical engineering and many other practical problems [39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

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D-Type Iterative Learning Control for Open Container Motion System With Sloshing Constraints

Li¹,

Hou

Yong³

2021

IEEE Access

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In this paper, the iterative learning control (ILC) problem is investigated for the motion system of an open container with sloshing constraints in industrial fluid packaging. Initially, a broader class of second-order in space and first-order in time linear time-invariant singular distributed parameter system is decomposed by analyzing the motion system of an open container with sloshing constraints. Meanwhile, to eliminate the influence of singular terms on the system, a closed-loop D-type ILC algorithm is designed, and the corresponding convergence conditions are manifested. Then the convergence of the control algorithm is proved strictly. The resulting tracking error of systems can converge to any small tracking accuracy. Finally, a numerical example is given to verify the convergence and effectiveness of the closed-loop D-type ILC algorithm.

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“…Liu et al [26] proposed a charging demand simulation method based on the Agent-cellular automata model to describe the changes in location and the state of charge of a moving EV. Li et al [27] used the iterative learning control algorithm to deal with generalized distributed parameter system with parabolic type described by generalized partial differential equation. Yang [28] used NetLogo simulation platform, using the overall modeling and simulation method of Multi-Agent, established the asymmetric evolutionary game simulation model which participates in the three parties, and run the model under different revenue parameters.…”

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confidence: 99%

Special issue on emergence in human-like intelligence toward cyber-physical systems

Yen

2019

Neural Comput & Applic

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As the famous slogan ''Connecting People'' indicates, a lot of the developments in novel technologies intensify the relationship between people without necessarily enhancing technologies that are close to the nature of human beings. Examples can be easily found in recent computing paradigms, such as Cloud Computing that advances network infrastructure for data storage and resource sharing, or the Internet of Things that investigates the intelligence and awareness of objects involved in the network. From this point of view, the existing state-of-the-art solutions, in realms of artificial intelligence and/or computational intelligence, significantly differ from the human competence level in many research domains. Even though it is generally not clear whether human-like approach would show its upper-hand over existing methods, the exploration of this research path seems to be advantageous and challenging. Human-like intelligence is arguably one of the most powerful approaches for the optimization and design of complex Web, as well as its correlated issues so as to meet the requirements and growing needs of new challenging applications (and services as well). Although the state of the art behind the computational intelligence has already reached an impressive scale, there remain quite a lot of attractive research topics, with consideration of human-like or human-oriented factors, for further and promising developments.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues.

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Iterative learning control for linear generalized distributed parameter system

Cited by 9 publications

References 25 publications

The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping

The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping

D-Type Iterative Learning Control for Open Container Motion System With Sloshing Constraints

Special issue on emergence in human-like intelligence toward cyber-physical systems

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