Zhixian Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Zhixian Liu has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Xiaofang Yuan
Hunan University
6 shared publications - Guoming Huang
College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, CHINA
4 shared publications - Ke Shi
Hunan University
2 shared publications - Xiru Wu
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
2 shared publications - Jinlei Wang
College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China
1 shared publication - Xizheng Zhang
School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, China
1 shared publication
Latest Publications
- 28 August 2023
- Published by Elsevier in ISA Transactions
- Vol. 143, 131-143
An adaptive collision avoidance strategy for autonomous vehicle under various road friction and speed
- 28 August 2023
- Published by Elsevier in ISA Transactions
- Vol. 143, 131-143
An adaptive collision avoidance strategy for autonomous vehicle under various road friction and speed
- 1 June 2021
- Published by Elsevier in ISA Transactions
- Vol. 112, 176-185
Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities
- 1 June 2021
- Published by Elsevier in ISA Transactions
- Vol. 112, 176-185
Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities
- 6 August 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Intelligent Vehicles
- Vol. 5 (4) , 626-634
Adaptivity-Enhanced Path Tracking System for Autonomous Vehicles at High Speeds
The path tracking is a basic function of the autonomous vehicle. Generally, it is difficult to realize the path tracking with high accuracy at high speeds. To solve this problem, an adaptivity-enhanced path tracking system (AEPTS) is investigated in this paper. The adaptive performance of this AEPTS is realized via three aspects. The first is to achieve preview distance adaptivity by a dynamic multi-point preview model (DMPPM), which provides the desired course deviation according to the vehicle speed and the reference path. The second is to improve the adaptivity of an adaptive fuzzy steering controller (AFSC) with an adaptive steering gain model which is built via the path curvature and vehicle speed. The third is that an optimal parameter surface is devised to enhance the adaptivity of the AEPTS at high speeds. Such a surface can provide the optimal coefficients for the AEPTS, and it is implemented using the particle swarm optimization (PSO) and a curve fitting method. At last, simulation experiments are given to verify the adaptivity of the proposed method.
- 6 August 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Intelligent Vehicles
- Vol. 5 (4) , 626-634
Adaptivity-Enhanced Path Tracking System for Autonomous Vehicles at High Speeds
The path tracking is a basic function of the autonomous vehicle. Generally, it is difficult to realize the path tracking with high accuracy at high speeds. To solve this problem, an adaptivity-enhanced path tracking system (AEPTS) is investigated in this paper. The adaptive performance of this AEPTS is realized via three aspects. The first is to achieve preview distance adaptivity by a dynamic multi-point preview model (DMPPM), which provides the desired course deviation according to the vehicle speed and the reference path. The second is to improve the adaptivity of an adaptive fuzzy steering controller (AFSC) with an adaptive steering gain model which is built via the path curvature and vehicle speed. The third is that an optimal parameter surface is devised to enhance the adaptivity of the AEPTS at high speeds. Such a surface can provide the optimal coefficients for the AEPTS, and it is implemented using the particle swarm optimization (PSO) and a curve fitting method. At last, simulation experiments are given to verify the adaptivity of the proposed method.
- 20 September 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ASME Transactions on Mechatronics
- Vol. 24 (6) , 2768-2778
Compensation-Based Robust Decoupling Control System for the Lateral and Longitudinal Stability of Distributed Drive Electric Vehicle
The lateral and longitudinal stability is important and difficult for distributed drive electric vehicle (DDEV). Current control methods for the lateral and longitudinal stability are designed based on the static model and parameter without considering the uncertain characteristic in DDEV. To solve this problem, this paper presents a novel compensation-based robust decoupling control system (CRDCS), which consists of a robust decoupling controller (RDC) and a feedback compensation controller (FCC). The RDC can achieve the robust stability for DDEV and obtain the decoupling control signals, using a robust inverse model constructed by a radial basis function neural network (RBF-NN). The FCC can eliminate the control error caused by the uncertain characteristic in DDEV, utilizing the compensation control signals obtained by the designed reference model and the model predictive controller (MPC). The simulation is carried on the Carsim and Matlab, and the results show that the proposed CRDCS has good robust performance for the lateral and longitudinal stability of DDEV.
- 20 September 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ASME Transactions on Mechatronics
- Vol. 24 (6) , 2768-2778
Compensation-Based Robust Decoupling Control System for the Lateral and Longitudinal Stability of Distributed Drive Electric Vehicle
The lateral and longitudinal stability is important and difficult for distributed drive electric vehicle (DDEV). Current control methods for the lateral and longitudinal stability are designed based on the static model and parameter without considering the uncertain characteristic in DDEV. To solve this problem, this paper presents a novel compensation-based robust decoupling control system (CRDCS), which consists of a robust decoupling controller (RDC) and a feedback compensation controller (FCC). The RDC can achieve the robust stability for DDEV and obtain the decoupling control signals, using a robust inverse model constructed by a radial basis function neural network (RBF-NN). The FCC can eliminate the control error caused by the uncertain characteristic in DDEV, utilizing the compensation control signals obtained by the designed reference model and the model predictive controller (MPC). The simulation is carried on the Carsim and Matlab, and the results show that the proposed CRDCS has good robust performance for the lateral and longitudinal stability of DDEV.