Abstract
Backing up truck-trailer vehicles is a required task in several industry sectors. Controllers that have been proposed to automate this process usually struggle when the vehicle is required to follow a nonlinear trajectory. This paper presents a neuro-fuzzy controller based on preview control and deep reinforcement learning for reverse parking truck-trailer vehicles. The controller consists of a deep neural network trained with reinforcement learning. A preview control signal is coupled into the trained controller to improve the control performance when tracking complex trajectories. Moreover, a fuzzy logic approach is used to avoid the jackknife state. Simulation results are presented to show that the controller is able to track circular and sinusoidal trajectories.
| Original language | Spanish |
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| Title of host publication | 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019 |
| State | Published - 1 May 2019 |
| Externally published | Yes |