Self-Tuning PID Controller Using a Neural Network for Nonlinear Exoskeleton System

Belov, Mikhail P.; Truong, Dinh Dang; Tuan, Pham Van

doi:10.1109/neuront53022.2021.9472852

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Due to the possession of non-linearity, and the ability of online learning (transferring the real-time experience to AI models), the AI-aided PI/PR online control methods have been widely used in different areas, such as dc-dc converter [32], [3], [33], [34], spacecraft [35], exoskeleton systems [36], and electric vehicle charging system [37]. However, there are generally two groups of approaches utilized to do that, design and control, as discussed below.…”

Section: Ai Applications In Linear Controllers Of Power Convertersmentioning

confidence: 99%

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Gao,

Wang,

Dragicevic

et al. 2023

IEEE Open J. Ind. Applicat.

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The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this paper presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This paper provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using datadriven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This paper discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

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Section: Ai Applications In Linear Controllers Of Power Convertersmentioning

confidence: 99%

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Gao,

Wang,

Dragicevic

et al. 2023

IEEE Open J. Ind. Applicat.

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show abstract

A neural network PID controller with dynamic structure adjustment for DC-DC switching converters

Liu

Wei

Chen

et al. 2022

2022 7th International Conference on Integrated Circuits and Microsystems (ICICM)

Self-Tuning PID Controller Using a Neural Network for Nonlinear Exoskeleton System

Cited by 3 publications

References 7 publications

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

A neural network PID controller with dynamic structure adjustment for DC-DC switching converters

Application of the Neural Network Approach when Tuning the PID Controller

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