Engineering topics

Wang, Zejiang

Publications and source records attributed to Wang, Zejiang.

10 records

DOE OSTI2024

Comparative Evaluation of Control-Oriented Heavy Duty Vehicle Air Drag Coefficient Models

Heavy-duty vehicles (HDVs) are a significant source of fuel consumption and greenhouse gas emissions, prompting solutions such as HDV platooning to mitigate these negative impacts through air drag reduction. The intervehicle distance in an HDV platoon needs to be carefully selected, such that the platoon-level energy efficiency and safety considerations can be well balanced. Underlying this problem lies in accurately modeling the relationship between HDV air drag coefficient and intervehicle distance. Through comprehensive evaluation and comparison, we analyze five control-oriented HDV air drag coefficient models, including the polynomial model, rational polynomial model, rational model, semi-quadratic model, and ridge model. Leveraging Scipy Curve-Fit toolbox and our previously compiled air drag coefficient datasets, we optimally identify the parameters inside each model. The calibrated models are then thoroughly evaluated via five complementary metrics. The comparison results reveal that the semi-quadratic model has the highest overall performance, while the widely adopted rational model only exhibits suboptimal performance.

Best, Micah↗

DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Human-Machine Shared Control for Path Following Considering Driver Fatigue Characteristics

Fatigue driving has been regarded as one of the most important factors that cause traffic accidents. This paper proposes a robust human-machine shared control strategy to improve the vehicle performance for different driver fatigue states. Firstly, the time-varying driver steering model is proposed to address the model mismatch caused by fatigue driving. And the driver fatigue evaluation system is established based on facial features to quantify driver fatigue levels. Based on the quantified fatigue levels, a novel strategy for allocating authorities of the driver and controller is developed for building the driver-vehicle interaction system. Then, to weaken the influence of parameter perturbations caused by the time-varying driver states, we design a fatigue-based shared controller through state feedback. The actuator saturation and system constraints are considered in the controller design through the robust set-invariance property to improve vehicle safety and driving comfort. The driver-in-the-loop platform is conducted to validate the effectiveness of the proposed shared steering controller. In conclusion, the experimental results show that the proposed strategy can adaptively optimize the human-machine authorities according to fatigue states and comprehensively improve vehicle performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Active Suspension Parameters Identification: An Algebraic Approach and its Application to Suspension Travel Control

The parameters of an active suspension need to be identified online, such that the suspension control system can be adapted to mechanical wear and load change. Recursive least squares and observer-based methods are frequently utilized to fulfill this purpose. However, they can yield slow parameter identification due to their asymptotic nature. We propose an algebraic identifier to estimate the parameters of an active suspension online, which does not maintain an asymptotic convergence phase. Simulation results demonstrate the effectiveness of the proposed algebraic approach.

Wang, Zejiang↗

DOE OSTI2023

Ground Vehicle Generalized Forces and Moment Governor Design Via Noncertainty-Equivalent Adaptive Prescribed Performance Control

Torque-vectoring technology demonstrates great potential in improving the safety and performance of ground vehicles. In this paper, a novel generalized forces and moment governor for torque vectoring is suggested. The proposed solution strategically combines prescribed performance control, noncertainty equivalent adaptive control design, and a smooth projection operator. The main advantage of the proposed control strategy lies in its capability to guarantee both the transient performance and prompt recovery of the desired deterministic behavior of the closed-loop adaptive system, even in the presence of parametric uncertainties. ASM simulation results are presented to validate the efficacy of the proposed generalized forces and moment governor and to demonstrate its superiority over a baseline solution.

Zhou, Xingyu↗

DOE OSTI2023

Adaptive Path-Following Control for Ground Vehicles Using a Switching Non-Quadratic Lyapunov Function

The application of adaptive control techniques in the development of control systems for intelligent vehicles, especially for ground vehicle path-following controllers, has gained popularity due to their ability to handle large-scale parametric uncertainties. However, the use of a standard quadratic Lyapunov function in existing adaptive control-based path-following controllers can lead to poor transient performance, such as slow convergence and/or large overshoot. To address this limitation, this study proposes the use of a switching non-quadratic Lyapunov function to design a model reference adaptive path-following controller that aims to provide superior transient performance. The stability and signal convergence of the closed-loop system are demonstrated through a Lyapunov-like analysis. Through dSPACE ASM simulation, the effectiveness of the proposed controller is illustrated, which confirms improved tracking performance over a baseline solution.

Zhou, Xingyu↗

DOE OSTI2023

Energy-Centric Cooperative Onramp Merging Strategy: An Analytical Solution

Cooperative highway onramp merging plays an important role in mitigating highway traffic congestion. A crucial component of a cooperative merging system is the merging sequence strategy, which determines each merging participant's order to reach the merging point. Existing merging sequence strategies can be classified into rule-based and optimization-based approaches. The rule-based strategies can be effortlessly implemented with a light online computational burden. However, they may not achieve the optimal energy efficiency. In contrast, the optimization-based strategies can yield the optimal merging sequence to minimize fuel consumption, but typically involve computationally expensive numerical optimization. To leverage the advantages from both sides, we propose a novel merging sequence strategy that can minimize fuel consumption while avoiding online numerical optimization. The key idea is to analytically formulate the expected fuel consumption of each merging participant. Using a realistic highway onramp scenario based on the NGSIM dataset, we validate the performance and the computational efficiency of the proposed merging sequence strategy via SUMO/SIMULINK joint simulation.

Wang, Zejiang↗

DOE OSTI2023

Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.

Zhou, Anye↗

DOE OSTI2023

Reducing Conservativeness of Polytopic Linear-Parameter-Varying Robust Vehicle Sideslip Angle Observer Through Minimum-Area Convex Quadrilateral Design

The polytopic linear-parameter-varying (LPV) method has become increasingly popular for designing intelligent control and estimation systems for ground vehicles, particularly for the vehicle sideslip angle observer. In vehicle lateral dynamics, the vehicle longitudinal velocity-induced nonlinearities are conventionally outer-approximated with polytopes such as rectangles or triangles to obtain a polytopic LPV system. Yet, such polytopic approximations tend to be conservative and may lead to inadequate observer performance. To address this issue, a minimum-area convex quadrilateral construction is proposed in this paper to reduce design conservatism. The suggested design is demonstrated through the synthesis of an LPV H ∞ robust vehicle sideslip angle observer. Furthermore, a dSPACE-ASM simulation study is conducted to demonstrate the effectiveness and the advantage of the proposed polytopic construction over a baseline approach.

Zhou, Xingyu↗