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Wang, Junmin

Publications and source records attributed to Wang, Junmin.

9 records
All sourcesNASA NTRSDOE OSTI
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

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↗
DOE OSTI2023

Ground vehicle lane-keeping assistance system via differential flatness output feedback control and algebraic derivative estimation

Vehicle run-off-road is one of the most frequent and fatal traffic accidents in the United States. Various lane-keeping assistance (LKA) systems have been developed in the last decade to help drivers stay on the road. Most of them are built upon linear driver–vehicle–road (DVR) models and treat road curvature as a disturbance. Albeit effective, their control performance would degrade if road curvature varies rapidly. Here, this paper proposes a novel nonlinear DVR model by integrating a driver steering model into vehicle–road kinematics, which explicitly considers road curvature. Particularly, this nonlinear DVR system has been proven to be differentially flat, and a flatness-based LKA system is designed. Additionally, Model-Free Control is introduced to compensate for system modeling errors. Hardware-in-the-loop simulations and diver-in-the-loop experiments validate the proposed control framework and demonstrate the performance enhancement with respect to a representative linear robust LKA system.

42 ENGINEERING↗
DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗
DOE OSTI2022

Coordinated Steering Angle and Yaw Moment Distribution to Increase Vehicle Regenerative Energy in Autonomous Driving

This paper presents an algorithm that increases vehicle regenerative braking energy recovery in autonomous driving by leveraging connected and automated vehicle (CAV) technology. Autonomous vehicles may select different maneuvers to increase kinetic energy recovery during deceleration. In the proposed algorithm, the cornering resistance that influences regenerative energy and energy consumption is defined in terms of steering angle during regenerative braking. In particular, a model predictive controller that distributes control inputs of the vehicle is adopted to reduce the cornering resistance for increasing regenerative braking torque. Utilizing the information from CAV technology, this algorithm enables the vehicle to safely conduct braking while increasing its regenerative energy recovery. CarSim-Simulink joint simulations demonstrate the effectiveness of the proposed method.

Choi, Junghyun↗