PID Optimization Using Lagrangian Mechanics
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916354462842880 |
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| author | Kou, Ethan Moghadam, Majid |
| author_facet | Kou, Ethan Moghadam, Majid |
| contents | Creating a simulation of a system enables the tuning of control systems without the need for a physical system. In this paper, we employ Lagrangian Mechanics to derive a set of equations to simulate an inverted pendulum on a cart. The system consists of a freely-rotating rod attached to a cart, with the rod's balance achieved through applying the correct forces to the cart. We manually tune the proportional, integral, and derivative gain coefficients of a Proportional Integral Derivative controller (PID) to balance a rod. To further improve PID performance, we can optimize an objective function to find better gain coefficients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00016 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PID Optimization Using Lagrangian Mechanics Kou, Ethan Moghadam, Majid Systems and Control Robotics Creating a simulation of a system enables the tuning of control systems without the need for a physical system. In this paper, we employ Lagrangian Mechanics to derive a set of equations to simulate an inverted pendulum on a cart. The system consists of a freely-rotating rod attached to a cart, with the rod's balance achieved through applying the correct forces to the cart. We manually tune the proportional, integral, and derivative gain coefficients of a Proportional Integral Derivative controller (PID) to balance a rod. To further improve PID performance, we can optimize an objective function to find better gain coefficients. |
| title | PID Optimization Using Lagrangian Mechanics |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2310.00016 |