Quadcopter pid controller design and path planning using bio-inspired meta-heuristic algorithms
| dc.contributor.advisor | Mahdy, Ahmed | |
| dc.contributor.advisor | Sheta, Alaa A. | |
| dc.contributor.author | Maddi, Dheeraj Reddy | |
| dc.contributor.committeeMember | Yadav, Mamta | |
| dc.creator.orcid | https://orcid.org/0000-0001-7941-5193 | |
| dc.creator.orcid | 0000-0001-7941-5193 | en_US |
| dc.date.accessioned | 2020-12-16T21:13:33Z | |
| dc.date.available | 2020-12-16T21:13:33Z | |
| dc.date.issued | 2020-05 | |
| dc.description.abstract | The usage of Quadcopter in commercial fields has evolved significantly due to its phenomenal development. However, controlling the movements of a Quadcopter is a demanding task due to its complex dynamics. The usage of the Proportional-Integral-Derivative(PID) controller for stability control is quite challenging in regards to the complexity of Quadcopter’s nonlinear structure. Conventional methods like Ziegler-Nichols(ZN) for tuning the PID controller for a Quadcopter do not provide efficient performance and might also cause the system to be severely damaged. In this thesis, we are addressing the problem of the controlling a Quadcopter using Metaheuristic-based PID controller. Multi-Objective Fitness Function is proposed to reduce the overall time of the step response effectively. Path planning is one of the important concepts for a Quadcopter to move from one point to another point effectively. A novel Neighborhood Search Genetic Algorithm (NSGA) is presented for path planning by balancing the diversity inside the Genetic Algorithm using a Neighborhood Search to produce an efficient path. The performance of the NSGA has been compared to traditional A∗ , standard GA, and PSO. NSGA produced superior results in terms of cost. | en_US |
| dc.description.college | College of Science and Engineering | en_US |
| dc.description.department | Computing Sciences | en_US |
| dc.format.extent | 79 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89234 | |
| dc.language.iso | en | en_US |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | en_US |
| dc.rights.holder | Maddi, Dheeraj Reddy | |
| dc.subject | Genetic Algorithm | en_US |
| dc.subject | Particle Swarm Optimization | en_US |
| dc.subject | PID Controller | en_US |
| dc.title | Quadcopter pid controller design and path planning using bio-inspired meta-heuristic algorithms | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | Texas A & M University--Corpus Christi | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |