Improvements of direct current motor control and motion trajectory algorithm development for automated guided vehicle

Abstract

The precise control of the two-wheel independent drive motor speed of automated guided vehicle is the prerequisite for high-precision operation of its actuator. The optimization of motor control performance has been a topic of concern to researchers. First, the model identification of direct current motor is done. Second, there are two control strategies, including parallel proportional-integral-differential control strategy and the coupling proportional-integral-differential control strategy, which are researched and compared for the direct current motor control system of automated guided vehicle in dispatching the goods from one location to another with greater ease. Finally, the motion trajectory algorithm of automated guided vehicle is also proposed on the basis of kinematics theory. Besides, the above control strategies and algorithm improved for automated guided vehicle are simulated by the MATLAB tool and the experimental simulations by Kinect sensor, and the simulation results show that the coupling proportional-integral-differential control strategy is slightly better in controlling performance because of some smaller cumulative errors and the better start performance. Meanwhile, the motion trajectory algorithm of automated guided vehicle under both the control strategies can achieve different effects in different control cases.

Position - Velocity Control for Two PMDC Motors Connected by a Cross-Coupling Technique with Butterflies Optimization Algorithm

“…This controller consists of a non-linear PID controller [20] . Panlong Zhang and Zhongcheng Introduced 2019 a speed control system for a two-wheel independent DC motor for automatic guided vehicles and tested to move from one location to another [15]. In 2020 JongNam Bae et al presented a parallel position control system for dual DC motor with Hall sensor.…”

Robust ℋ∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

“…In the case where structural model errors (i.e., deficiencies or artifacts in the selected model [30,31]) are more dominating than the effects from environments or noise disturbances, accurate system modelling will be highly instrumental in synthesizing the best compensator. Treating the uncertainties in both linear and nonlinear models are equally crucial for enhancing the tracking performance under various circumstances particularly when the system involves multiple DC motors, such as those in wheeled mobile robots (WMRs) [32,33]. Plus, to ensure the compensator is robust against the uncertainties, the source of variations in the model parameters needs to be identified and made auxiliary to the control scheme.…”