Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control
| dc.contributor.advisor | Tsoeu, Mohohlo S. | |
| dc.contributor.author | Gwayi, Isaac | |
| dc.date.accessioned | 2019-05-10T12:11:15Z | |
| dc.date.available | 2019-05-10T12:11:15Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-05-07T09:16:37Z | |
| dc.description.abstract | Vehicle safety remains an important topic in the automotive industry due to the large number of vehicle accidents each year. One of the causes of vehicle accidents is due to vehicle instability phenomena. Vehicle instability can occur due to unexpected road profile changes, during full braking, obstacle avoidance or severe manoeuvring. Three main instability phenomena can be distinguished: the yaw-rate instability, the rollover and the jack-knife phenomenon. The main goal of this study is to develop a yaw-rate and rollover stability controller of an Autonomous Scaled Ground Vehicle (ASGV) using Nonlinear Model Predictive Control (NMPC). Open Source Software (OSS) known as Automatic Control and Dynamic Optimisation (ACADO) is used to design and simulate the NMPC controller based on an eight Degree of Freedom (8 DOF) nonlinear vehicle model with Pacejka tire model. Vehicle stability limit were determined using load transfer ratio (LTR). Double lane change (DLC) steering manoeuvres were used to calculate the LTR. The simulation results show that the designed NMPC controller is able to track a given trajectory while preventing the vehicle from rolling over and spinning out by respecting given constraints. A maximum trajectory tracking error of 0.1 meters (on average) is reported. To test robustness of the designed NMPC controller to model mismatch, four simulation scenarios are done. Simulation results show that the controller is robust to model mismatch. To test disturbance rejection capability of the controller, two simulations are performed, with pulse disturbances of 0.02 radians and 0.05 radians. Simulations results show that the controller is able to reject the 0.02 radians disturbance. The controller is not able to reject the 0.05 radians disturbance. | |
| dc.identifier.apacitation | Gwayi, I. (2018). <i>Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control</i>. (). ,Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/30054 | en_ZA |
| dc.identifier.chicagocitation | Gwayi, Isaac. <i>"Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control."</i> ., ,Engineering and the Built Environment ,Department of Electrical Engineering, 2018. http://hdl.handle.net/11427/30054 | en_ZA |
| dc.identifier.citation | Gwayi, I. 2018. Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control. . ,Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/30054 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Gwayi, Isaac AB - Vehicle safety remains an important topic in the automotive industry due to the large number of vehicle accidents each year. One of the causes of vehicle accidents is due to vehicle instability phenomena. Vehicle instability can occur due to unexpected road profile changes, during full braking, obstacle avoidance or severe manoeuvring. Three main instability phenomena can be distinguished: the yaw-rate instability, the rollover and the jack-knife phenomenon. The main goal of this study is to develop a yaw-rate and rollover stability controller of an Autonomous Scaled Ground Vehicle (ASGV) using Nonlinear Model Predictive Control (NMPC). Open Source Software (OSS) known as Automatic Control and Dynamic Optimisation (ACADO) is used to design and simulate the NMPC controller based on an eight Degree of Freedom (8 DOF) nonlinear vehicle model with Pacejka tire model. Vehicle stability limit were determined using load transfer ratio (LTR). Double lane change (DLC) steering manoeuvres were used to calculate the LTR. The simulation results show that the designed NMPC controller is able to track a given trajectory while preventing the vehicle from rolling over and spinning out by respecting given constraints. A maximum trajectory tracking error of 0.1 meters (on average) is reported. To test robustness of the designed NMPC controller to model mismatch, four simulation scenarios are done. Simulation results show that the controller is robust to model mismatch. To test disturbance rejection capability of the controller, two simulations are performed, with pulse disturbances of 0.02 radians and 0.05 radians. Simulations results show that the controller is able to reject the 0.02 radians disturbance. The controller is not able to reject the 0.05 radians disturbance. DA - 2018 DB - OpenUCT DP - University of Cape Town KW - Engineering LK - https://open.uct.ac.za PY - 2018 T1 - Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control TI - Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control UR - http://hdl.handle.net/11427/30054 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/30054 | |
| dc.identifier.vancouvercitation | Gwayi I. Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control. []. ,Engineering and the Built Environment ,Department of Electrical Engineering, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/30054 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.subject | Engineering | |
| dc.title | Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc (Engineering) |