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Development of Fuzzy Inference System-Based Control Strategy for Various Autonomous Platforms

Choi, Daegyun
http://orcid.org/0000-0002-7149-0890
http://rave.ohiolink.edu/etdc/view?acc_num=ucin169227302512029

Abstract Details

2023, PhD, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
Conventional control approaches have been developed based on mathematical models of systems that contain multiple user-defined parameters, and it is time-consuming to determine such parameters. With advancements in computing power, artificial intelligence (AI) has been recently used to control autonomous systems. However, it is difficult for engineers to understand how the resulting output is obtained because most AI techniques are a black box without defining a mathematical model. On the other hand, a fuzzy inference system (FIS) is a preferable option because of its explainability. By adding learning capability to the FIS using a genetic algorithm (GA), the FIS can provide a near-optimal solution, which is known as a genetic fuzzy system (GFS). To exploit the advantages of the GFS, this work develops the FIS-based control approaches for diverse autonomous platforms, which include aerial, ground, and space platforms. For aerial platforms, this work develops a FIS-applied collision avoidance (CA) algorithm that can provide a near-optimal solution in terms of the travel distance of unmanned aerial vehicles (UAVs). After introducing a compact form of equations, which reduces the number of unknown parameters from 6 to 2, based on the enhanced potential field (EPF) approach, the proposed FIS models determine two unknowns, which are the magnitude of the avoidance maneuvers. The proposed models are trained to overcome the drawbacks of the artificial potential field (APF) while minimizing the travel distance of the UAVs, the trained FIS models are tested in a complex environment in the presence of multiple static and dynamic obstacles by increasing the number of UAVs in a given area. Numerical simulation results are presented for the training and testing results, including the comparison with the EPF. For ground platforms, this work proposes a decentralized multi-robot system (MRS) control approach to perform a collaborative object transportation with a near-optimal navigation solution in unstructured environment, like Martian surface, while maintaining the object's attitude stably. The proposed FIS models directly determine the vector heading to the target location and the vector avoiding the non-traversable area where the MRS cannot maintain the object's stable attitude. To validate the performance of the proposed models, the training results are compared with the path optimization results. Then, the results tested in an arbitrarily generated unstructured environment are illustrated for multiple scenarios. For space platforms, to support in-space servicing or space debris removal missions, this work designs a FIS-based controller for a chaser to approach a moving target, orbiting the Earth, in the rendezvous process while minimizing energy consumption. To consider practical situations, physical constraints, such as a thruster limitation, a keep-out zone surrounding the target, and a docking corridor, which is a safe zone, are employed in the formulations. The proposed FIS controller determines the force for approaching the target and the repelling force to ensure the chaser keeps inside the docking corridor. Although the proposed controller is trained with no disturbance condition, the Monte Carlo simulation results containing the disturbance are presented for several initial conditions.
Donghoon Kim, Ph.D. (Committee Chair)
Anoop Sathyan, Ph.D. (Committee Member)
Ou Ma, Ph.D. (Committee Member)
Kelly Cohen, Ph.D. (Committee Member)
120 p.
Aerospace Engineering
Intelligent Control; Fuzzy Inference System; Collision Avoidance; Multi-Robot Coordination; Spacecraft Rendezvous and Proximity Operation; Genetic Fuzzy System

Recommended Citations

Citations

  • Choi, D. (2023). Development of Fuzzy Inference System-Based Control Strategy for Various Autonomous Platforms [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin169227302512029

    APA Style (7th edition)

  • Choi, Daegyun. Development of Fuzzy Inference System-Based Control Strategy for Various Autonomous Platforms. 2023. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin169227302512029.

    MLA Style (8th edition)

  • Choi, Daegyun. "Development of Fuzzy Inference System-Based Control Strategy for Various Autonomous Platforms." Doctoral dissertation, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin169227302512029

    Chicago Manual of Style (17th edition)

ucin169227302512029
94
© 2023, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.