With the rapid growth of logistics transportation in the framework of Industry 4.0,
automated guided vehicle (AGV) technologies have developed speedily. These systems present two coupled control problems: the control of the longitudinal velocity,
essential to ensure the application requirements such as throughput and tag time,
and the trajectory tracking control, necessary to ensure the proper accuracy in loading and unloading manoeuvres. When the paths are very short or have abrupt
changes, the kinematic constraints play a restrictive role, and the tracking control
becomes more challenging. In this case, advanced control strategies such as those
based on intelligent techniques, including machine learning (ML) can be useful.
Hence, in this work, we present an intelligent hybrid control scheme that combines
reinforcement learning-based control (RLC) with conventional PI regulators to face
both control problems simultaneously. On the one hand, PIs are used to control the
speed of each wheel. On the other hand, the input reference of these regulators is
calculated by the RLC in order to reduce the guiding error of the path tracking and to
maintain the longitudinal speed. The latter is compared with a PID path following
controller. The PID regulators have been tuned by genetic algorithms. The RLC allows
the vehicle to learn how to improve the trajectory tracking in an adaptive way and
thus, the AGV can face disturbances or unknown physical system parameters that
may change due to friction and degradation of AGV mechanical components. Extensive simulation experiments of the proposed intelligent control strategy on a hybrid
tricycle and differential AGV model, that considers the kinematics and the dynamics
of the vehicle, prove the efficiency of the approach when following different
demanding trajectories. The performance of the RL tracking controller in comparison
with the optimized PID gives errors around 70% smaller, and the average maximum
error is also 48% lower.
