Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
This paper presents the parameter optimisation of the flight control system of a singlerotor medium-scale rotorcraft. The six degrees-of-freedom (DOF) nonlinear mathematical model of the rotorcraft is developed. This model is then used to develop proportional–integral–derivative (PID)-based controll...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/14/6/178 |
| _version_ | 1797531491151904768 |
|---|---|
| author | Lindokuhle J. Mpanza Jimoh Olarewaju Pedro |
| author_facet | Lindokuhle J. Mpanza Jimoh Olarewaju Pedro |
| author_sort | Lindokuhle J. Mpanza |
| collection | DOAJ |
| description | This paper presents the parameter optimisation of the flight control system of a singlerotor medium-scale rotorcraft. The six degrees-of-freedom (DOF) nonlinear mathematical model of the rotorcraft is developed. This model is then used to develop proportional–integral–derivative (PID)-based controllers. Since the majority of PID controllers installed in industry are poorly tuned, this paper presents a comparison of the optimised tuning of the flight controller parameters using particle swarm optimisation (PSO), genetic algorithm (GA), ant colony optimisation (ACO) and cuckoo search (CS) optimisation algorithms. The aim is to find the best PID parameters that minimise the specified objective function. Two trim conditions are investigated, i.e., hover and 10 m/s forward flight. The four algorithms performed better than manual tuning of the PID controllers. It was found, through numerical simulation, that the ACO algorithm converges the fastest and finds the best gains for the selected objective function in hover trim conditions. However, for 10 m/s forward flight trim, the GA algorithm was found to be the best. Both the tuned flight controllers managed to reject a gust wind of up to 5 m/s in the lateral axis in hover and in forward flight. |
| first_indexed | 2024-03-10T10:44:33Z |
| format | Article |
| id | doaj.art-0fec5a2b22894091a99b38c637777988 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T10:44:33Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-0fec5a2b22894091a99b38c6377779882023-11-21T22:39:54ZengMDPI AGAlgorithms1999-48932021-06-0114617810.3390/a14060178Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale RotorcraftLindokuhle J. Mpanza0Jimoh Olarewaju Pedro1School of Mechanical, Industrial and Aeronautical Engineering, Faculty of Engineering and The Built Environment, University of the Witwatersrand, Johannesburg 2000, South AfricaSchool of Mechanical, Industrial and Aeronautical Engineering, Faculty of Engineering and The Built Environment, University of the Witwatersrand, Johannesburg 2000, South AfricaThis paper presents the parameter optimisation of the flight control system of a singlerotor medium-scale rotorcraft. The six degrees-of-freedom (DOF) nonlinear mathematical model of the rotorcraft is developed. This model is then used to develop proportional–integral–derivative (PID)-based controllers. Since the majority of PID controllers installed in industry are poorly tuned, this paper presents a comparison of the optimised tuning of the flight controller parameters using particle swarm optimisation (PSO), genetic algorithm (GA), ant colony optimisation (ACO) and cuckoo search (CS) optimisation algorithms. The aim is to find the best PID parameters that minimise the specified objective function. Two trim conditions are investigated, i.e., hover and 10 m/s forward flight. The four algorithms performed better than manual tuning of the PID controllers. It was found, through numerical simulation, that the ACO algorithm converges the fastest and finds the best gains for the selected objective function in hover trim conditions. However, for 10 m/s forward flight trim, the GA algorithm was found to be the best. Both the tuned flight controllers managed to reject a gust wind of up to 5 m/s in the lateral axis in hover and in forward flight.https://www.mdpi.com/1999-4893/14/6/178rotorcraft UAVoptimisationdynamic modellingant colony optimisationcuckoo searchgenetic algorithm |
| spellingShingle | Lindokuhle J. Mpanza Jimoh Olarewaju Pedro Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft Algorithms rotorcraft UAV optimisation dynamic modelling ant colony optimisation cuckoo search genetic algorithm |
| title | Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft |
| title_full | Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft |
| title_fullStr | Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft |
| title_full_unstemmed | Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft |
| title_short | Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft |
| title_sort | optimised tuning of a pid based flight controller for a medium scale rotorcraft |
| topic | rotorcraft UAV optimisation dynamic modelling ant colony optimisation cuckoo search genetic algorithm |
| url | https://www.mdpi.com/1999-4893/14/6/178 |
| work_keys_str_mv | AT lindokuhlejmpanza optimisedtuningofapidbasedflightcontrollerforamediumscalerotorcraft AT jimoholarewajupedro optimisedtuningofapidbasedflightcontrollerforamediumscalerotorcraft |