Key Takeaways & Executive Findings
- •• Proposes a projected interval unscented Kalman filter (PIUKF) that incorporates state constraints to accurately estimate vehicle sideslip angle and yaw rate, enhancing robustness. • Develops an explicit model predictive control (EMPC) strategy that significantly reduces online computational burden compared to traditional MPC, enabling real-time implementation. • Integrates communication delay compensation into the observer-based control framework, improving path following performance under realistic data transmission conditions. • Validates the proposed strategy through numerical simulations and hardware-in-the-loop tests, demonstrating its effectiveness and practicability for autonomous electric vehicles.
Abstract
The existing research on the path following of the autonomous electric vehicle (AEV) mainly focuses on the path planning and the kinematic control. However, the dynamic control with the state observation and the communication delay is usually ignored, so the path following performance of the AEV cannot be ensured. This article studies the observer-based path following control strategy for the AEV with the communication delay via a robust explicit model predictive control approach. Firstly, a projected interval unscented Kalman filter is proposed to observe the vehicle sideslip angle and yaw rate. The observer considers the state constraints during the observation process, and the robustness of the observer is also considered. Secondly, an explicit model predictive control is designed to reduce the computational complexity. Thirdly, considering the efficiency of the information transmission, the influence of the communication delay is considered when designing the observer-based path following control strategy. Finally, the numerical simulation and the hardware-in-the-loop test are conducted to examine the effectiveness and practicability of the proposed strategy.
1. Introduction
Due to the potential to reduce traffic accidents, reduce emissions, and improve the utilization of transportation resources, the autonomous electric vehicle (AEV) has been extensively studied in the past decades [1, 2]. One essential point of the AEV is the path following performance, which can ensure the vehicle to follow the given path with guaranteed vehicle stability [3].
The vehicle sideslip angle (VSA) and yaw rate (YR) of the AEV are two pivotal parameters for vehicle path following. However, in light of the physical and commercial considerations, the in-vehicle sensors are not appropriate for the measurement of the VSA and YR [4]. Thus, a state observer is necessary for the control system design [5]. To observe the VSA and YR, the Kalman filter is a good choice. Because the unscented Kalman filter (UKF) does not need to calculate Jacobian and Hessian matrices and has fine accuracy as compared to the extended Kalman filter, it has been widely utilized in the VSA and YR estimation in recent years [6, 7]. However, the constraints of the states are always ignored, resulting in the inaccurate estimation of the model states. Thus, a projection interval UKF (PIUKF) that avoids local linearization and incorporates state boundaries in its estimation of the VSA and YR is proposed.
In addition to the state observation, numerous control strategies have been studied for the path following control of the AEV, such as the sliding mode control [8], the fuzzy static output feedback control [9], the model predictive control (MPC) [10], etc. Compared with other control schemes mentioned above, the MPC permits the systematic incorporation of constraints and minimizes a cost function in a receding horizon approach. Hence, it has been extensively employed for the development of path following control in the last decade. To follow the path on slippery roads at the highest possible vehicle speed, the MPC has been proven to be effective on icy roads via experiments [11]. In Ref. [12], the linear MPC controller is designed with consideration of the path following error model, and the control action cooperates with the feedforward and robust aspects [13]. However, because the MPC needs to solve a programming issue at each sampling instant, its online computational burden is large [14]. The heavy computational burden leads to extra requirements on the processing power of the controller hardware and high cost [15].
The explicit MPC (EMPC) requires less online computational power compared to the traditional MPC in providing a similar performance [16]. The EMPC involves two main s
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Jing Zhao, Renbin Li, Mingze Lv, Wenfeng Li, Zhengchao Xie, Pak Kin Wong (2025). Observer-Based Robust Explicit Model Predictive Control for Path Following of Autonomous Electric Vehicles with Communication Delay. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01257-z
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes an observer-based robust explicit model predictive control strategy for path following of autonomous electric vehicles, incorporating a projected interval unscented Kalman filter for state estimation and explicitly addressing communication delay to enhance performance.
How does the proposed method handle computational complexity?
By using explicit model predictive control (EMPC), the online computational burden is significantly reduced compared to traditional MPC, as the optimal control law is pre-computed offline and implemented as a piecewise affine function.
What are the key parameters estimated by the observer?
The observer estimates the vehicle sideslip angle (VSA) and yaw rate (YR), which are critical for vehicle stability and path following but are difficult to measure directly with in-vehicle sensors.
How is communication delay addressed in the control strategy?
The control strategy explicitly considers the influence of communication delay in the design of the observer-based path following controller, ensuring robustness and maintaining performance under realistic data transmission conditions.
What validation methods were used in the study?
The proposed strategy was validated through numerical simulations and hardware-in-the-loop (HIL) tests, demonstrating its effectiveness and practicability for real-world implementation.
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