Enhanced Sliding Mode Controls for Autonomous Vehicles
This paper presents a solution to the steering and acceleration control of autonomous vehicles. Two parametric physics-based models, kinematic and dynamic, are developed for four-wheeled rear-wheel drive vehicles. A nonlinear control strategy is performed using sliding mode control. Three tuning str...
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/414371 |
| Acesso em linha: | https://hdl.handle.net/2117/414371 |
| Access Level: | acceso abierto |
| Palavra-chave: | Automated vehicles Vehicles autònoms Àrees temàtiques de la UPC::Enginyeria mecànica |
| Resumo: | This paper presents a solution to the steering and acceleration control of autonomous vehicles. Two parametric physics-based models, kinematic and dynamic, are developed for four-wheeled rear-wheel drive vehicles. A nonlinear control strategy is performed using sliding mode control. Three tuning strategies, pseudo-greedy search, genetic algorithms, and particle swarm optimization are applied to determine controller parameters. A comparison of sliding mode control and linear parameter-varying model predictive control is studied. Robustness against nonlinearity and parametric uncertainty in the model are the main advantages of the sliding control method. Nevertheless, chattering is the main drawback; thus, advanced design in the sliding surface is implemented. The proposed control strategies and tuning strategies are implemented with Matlab in a cloud virtual environment. Additionally, the theoretical development of linear matrix inequality-based sliding mode control using a dynamic model considering the effect of slip angle is presented. |
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