Towards Intelligent Control via Genetic Programming

Marchetti, Francesco; Minisci, Edmondo; Riccardi, Annalisa

doi:10.1109/ijcnn48605.2020.9207694

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Individuals with a higher fitness and ranking are more likely to progress to the next generation. There exists a set of usual genetic operations, also known as rules, that determine how individuals can be considered successful and evolve to the next generation [ 30 ]: elitism, replication, crossover, and mutation.…”

Section: Evolutionary Techniquesmentioning

confidence: 99%

Revisiting Classical Controller Design and Tuning with Genetic Programming

García,

Velasco,

Angulo

et al. 2023

Sensors

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This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP’s functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications.

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Section: Evolutionary Techniquesmentioning

confidence: 99%

Revisiting Classical Controller Design and Tuning with Genetic Programming

García,

Velasco,

Angulo

et al. 2023

Sensors

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“…An example of GP used in an IC framework is represented by the work of Chiang [27] where the control law for a small robot is produced online using GP, with the aim of moving the robot in an environment filled with obstacles. A similar approach was used in [28], where a guidance controller for a Goddard rocket is designed online to cope with different kinds of uncertainties. In contrast to this last example, the work presented here aims to generate a control law for a much more complex and nonlinear system, also considering more severe uncertainties.…”

Section: Genetic Programming For Controlmentioning

confidence: 99%

“…The work produced in [28] was later used as a foundation to design the Hybrid GP-NN controller presented in [29]. In this control system, the GP was used offline to generate a control law that was later optimized online by a NN.…”

Section: Genetic Programming For Controlmentioning

confidence: 99%

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Marchetti

Minisci

2021

Mathematics

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As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques.

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“…An approach similar to [49], which also uses Genetic Programming (GP), is proposed by Marchetti et al [50]. Here GP generates a control law online and the controller is tested on different failure scenarios.…”

Section: Classification Of Relevant Examplesmentioning

confidence: 99%

Classifying Intelligence in Machines: A Taxonomy of Intelligent Control

Wilson¹,

Marchetti²,

Carlo³

et al. 2020

Robotics

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The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent.

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Towards Intelligent Control via Genetic Programming

Cited by 5 publications

References 13 publications

Revisiting Classical Controller Design and Tuning with Genetic Programming

Revisiting Classical Controller Design and Tuning with Genetic Programming

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Classifying Intelligence in Machines: A Taxonomy of Intelligent Control

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