Difference between revisions of "Projects:2018s1-181 BMW Autonomous Vehicle"
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| − | == | + | ==Software Development== |
This projects main focus is on MPC development for a steering system of the BMW autonomous vehicle. During this project, Model Predictive Controller toolbox in MATLAB Simulink will be used to develop the MPC controller which suits in our project. In addition, the vehicle model which was provided by Robert Dollinger in 2016 could not be used in Model Predictive Controller Toolbox in MATLAB due to errors. Therefore, In the software part, a new vehicle model will be developed and a Model Predictive Controller will be developed and used in the simulation part of this project. New software call Driving Scenario Designer will also be used to develop the reference track for simulating the new vehicle model and controller. | This projects main focus is on MPC development for a steering system of the BMW autonomous vehicle. During this project, Model Predictive Controller toolbox in MATLAB Simulink will be used to develop the MPC controller which suits in our project. In addition, the vehicle model which was provided by Robert Dollinger in 2016 could not be used in Model Predictive Controller Toolbox in MATLAB due to errors. Therefore, In the software part, a new vehicle model will be developed and a Model Predictive Controller will be developed and used in the simulation part of this project. New software call Driving Scenario Designer will also be used to develop the reference track for simulating the new vehicle model and controller. | ||
| − | == | + | ==MPC Introduction== |
Stability and safety are the most important factors of autonomous driving. MPC is a controller which has 2 important advantages to achieve the stability and safety of the autonomous vehicle’s steering system. | Stability and safety are the most important factors of autonomous driving. MPC is a controller which has 2 important advantages to achieve the stability and safety of the autonomous vehicle’s steering system. | ||
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Although the result returned by MPC may not match the reference trajectory precisely, the result can still be optimized as hard as possible to get close to the given trajectory. | Although the result returned by MPC may not match the reference trajectory precisely, the result can still be optimized as hard as possible to get close to the given trajectory. | ||
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| − | == Vehicle Model == | + | ==Vehicle Model== |
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== First Level System Design'== | == First Level System Design'== | ||
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| + | ==MPC System Design== | ||
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| + | ==Simulation Results== | ||
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| − | == | + | ==Software Part Future Work== |
The MPC controller that we developed can only acquire good performance results when the speed is 18km/h. In some cases, there will be 1.5 meters oversteer on the Y position when the vehicle tries to track and approach the reference path at the beginning. Therefore, we thought this oversteer was caused by the set parameters which were not properly measured. These parameters include cornering stiffness and moment of inertia of the vehicle. Hence, as a further development, a parameter estimator may be required and developed. | The MPC controller that we developed can only acquire good performance results when the speed is 18km/h. In some cases, there will be 1.5 meters oversteer on the Y position when the vehicle tries to track and approach the reference path at the beginning. Therefore, we thought this oversteer was caused by the set parameters which were not properly measured. These parameters include cornering stiffness and moment of inertia of the vehicle. Hence, as a further development, a parameter estimator may be required and developed. | ||
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By using the vehicle model, we observed that when the vehicle's velocity get close to 0, the state space model becomes unstable which is another problem that needs to be solved in the future work. | By using the vehicle model, we observed that when the vehicle's velocity get close to 0, the state space model becomes unstable which is another problem that needs to be solved in the future work. | ||
| − | == | + | ==Hardware Part== |
...due Sunday night. | ...due Sunday night. | ||
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| + | ==Conclusion== | ||
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| − | + | ==References== | |
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Revision as of 19:53, 21 October 2018
Contents
Students
Corey Miller
Kaifeng Ren
Muhammad Sufyaan Bin Mohd Faiz
Ovini Amaya Perera
Yiduo Yin
Supervisors
Associate Professor Nesimi Ertugral
Mr. Robert Dollinger
Project Description
Software Development
This projects main focus is on MPC development for a steering system of the BMW autonomous vehicle. During this project, Model Predictive Controller toolbox in MATLAB Simulink will be used to develop the MPC controller which suits in our project. In addition, the vehicle model which was provided by Robert Dollinger in 2016 could not be used in Model Predictive Controller Toolbox in MATLAB due to errors. Therefore, In the software part, a new vehicle model will be developed and a Model Predictive Controller will be developed and used in the simulation part of this project. New software call Driving Scenario Designer will also be used to develop the reference track for simulating the new vehicle model and controller.
MPC Introduction
Stability and safety are the most important factors of autonomous driving. MPC is a controller which has 2 important advantages to achieve the stability and safety of the autonomous vehicle’s steering system.
■ Advantage1: Constraints Handling There are a lot of constraints (physical limitations) which need to be overcome when the car is driving on the road. MPC can handle constraints systematically and produce feasible solutions for our car. Its ability of constraints handling can provide stability and safety for our autonomous vehicle.
■ Advantage2: Optimization of the result Although the result returned by MPC may not match the reference trajectory precisely, the result can still be optimized as hard as possible to get close to the given trajectory.
Vehicle Model
First Level System Design'
MPC System Design
Simulation Results
Software Part Future Work
The MPC controller that we developed can only acquire good performance results when the speed is 18km/h. In some cases, there will be 1.5 meters oversteer on the Y position when the vehicle tries to track and approach the reference path at the beginning. Therefore, we thought this oversteer was caused by the set parameters which were not properly measured. These parameters include cornering stiffness and moment of inertia of the vehicle. Hence, as a further development, a parameter estimator may be required and developed.
Secondly, as I mentioned above that the vehicle can only perform well at 18km/h, so an adaptive MPC controller may be needed to allow the vehicle to follow the reference path at different speeds.
By using the vehicle model, we observed that when the vehicle's velocity get close to 0, the state space model becomes unstable which is another problem that needs to be solved in the future work.
Hardware Part
...due Sunday night.
