The article presents a speed-controlled driving mechanism designed for an electric vehicle, utilizing a dual Brushless Direct Current (BLDC) motor autonomous steering configuration. To achieve accurate control of the speed in motor control applications, it is desirable to ensure that the system operates with high efficiency and robustness for optimal performance. Traditional approaches to motor control, such as Integral-Derivative (PID) control, are often severely limited by the dynamic requirements of changing speeds and the effects of sensor noise, hence requiring new solutions. In response to these challenges, this work presents the combined usage of adaptive PID control techniques, fuzzy logic algorithms, and Controller Area Network (CAN) bus communication protocols to improve the speed and directional orientation of a dual-motor autonomous vehicle steering design, thereby guaranteeing both real-time response and high-performance control capabilities. The proposed approach of adaptive fuzzy-PID, which is based on the real-time analysis of motor behavior, empowers the system to dynamically adjust to the varying commands pertaining to motor speed and direction, thereby ensuring that optimal performance is consistently achieved across a spectrum of operational conditions. Furthermore, CAN communication facilitates both the effective control of the motors and the acquisition of diagnostic feedback, thus enabling the system to function efficiently in real-time environments while simultaneously monitoring critical safety features to assure reliable operational integrity. The proposed system has been experimentally validated and is designed to enable the independent control of dual motors with synchronized speed regulation, thereby rendering its application particularly suitable for various domains, including electric vehicles, robotic systems, and any other applications wherein coordinated motor movement is of utmost significance.
Shah et al. (Mon,) studied this question.