The Asian Journal of Control’s article, “Seven‐degree‐of‐freedom‐based electric wheel sampled‐data active shimmy control method considering unknown sensor measurement error,” delves into developing control mechanisms for electric vehicles driven by in-wheel motors (EV-DIM). This comprehensive study explores overcoming challenges related to sensor measurement errors and low-degree-of-freedom models. By employing a higher seven-degree-of-freedom (7DOF) shimmy model, the researchers aim to enhance the accuracy and effectiveness of shimmy control in electric vehicles.
Challenges in Current Shimmy Control Methods
Traditional active shimmy control methods for EV-DIM often rely on the presumption that sensor data is entirely accurate. This assumption neglects the possibility of measurement errors, complicating the control process. Additionally, prevailing models with low degrees of freedom simplify the control tasks but at the cost of reduced accuracy. Addressing these limitations is critical for the advancement of reliable shimmy control techniques.
In this study, researchers tackle these issues by developing a 7DOF model that encompasses the steering system, suspension, and electric wheel. Through the application of Lagrange’s theorem, they derive the dynamic equations necessary for this model. This approach promises a more detailed and accurate representation of the shimmy phenomenon, accounting for the intricacies of the electric wheel system.
Advanced Control Techniques
To mitigate the impact of unknown measurement errors, system state equations based on the 7DOF shimmy model are formulated. The researchers design a sampled-data observer and controller aimed at attenuating or completely eliminating the shimmy effect. By utilizing a domination gain, the proposed control method seeks to enhance vehicle stability and performance.
The effectiveness of this advanced control technique is verified through a series of numerical simulations and experiments. These tests demonstrate the potential of the proposed method to significantly improve shimmy control in electric vehicles, especially those utilizing in-wheel motors.
Earlier studies on shimmy control mostly revolved around simpler models and did not adequately address sensor inaccuracies. Previous research focused largely on mechanical solutions or basic control algorithms that could not fully counteract the shimmy effect, especially under erroneous sensor data. This paper’s approach contrasts sharply by integrating a comprehensive 7DOF model with advanced sampled-data control techniques.
Comparing this study with past research highlights a significant shift towards more sophisticated and accurate modeling of the shimmy phenomenon. The inclusion of sensor error considerations marks a substantial improvement over earlier methods, which largely overlooked this critical aspect. The development of a detailed 7DOF model represents a forward-thinking approach, providing a more realistic framework for addressing shimmy in electric vehicles.
The researchers’ work underscores the importance of accurate modeling and robust control mechanisms in enhancing the stability of electric vehicles. By addressing the challenges posed by sensor measurement errors and low-degree-of-freedom models, this study paves the way for more reliable and effective shimmy control methods. Future research could further refine these techniques, potentially leading to widespread implementation in the automotive industry.
