An Overview on System-Identification Problems in Vehicle Chassis Control

2012 ◽  
pp. 35-49
Author(s):  
Simone Formentin ◽  
Sergio M. Savaresi
Keyword(s):  
System Identification ◽  
Identification Problems ◽  
Chassis Control ◽  
Vehicle Chassis

scholarly journals Design of an Integrated Vehicle Chassis Control System with Driver Behavior Identification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bing Zhu ◽  
Yizhou Chen ◽  
Jian Zhao ◽  
Yunfu Su
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Driver Behavior ◽  
Stability Performance ◽  
Integrated Chassis Control ◽  
Yaw Moment Control ◽  
Chassis Control ◽  
Lane Change Test ◽  
Behavior Characteristics ◽  
Vehicle Chassis

An integrated vehicle chassis control strategy with driver behavior identification is introduced in this paper. In order to identify the different types of driver behavior characteristics, a driver behavior signals acquisition system was established using the dSPACE real-time simulation platform, and the driver inputs of 30 test drivers were collected under the double lane change test condition. Then, driver behavior characteristics were analyzed and identified based on the preview optimal curvature model through genetic algorithm and neural network method. Using it as a base, an integrated chassis control strategy with active front steering (AFS) and direct yaw moment control (DYC) considering driver characteristics was established by model predictive control (MPC) method. Finally, simulations were carried out to verify the control strategy by CarSim and MATLAB/Simulink. The results show that the proposed method enables the control system to adjust its parameters according to the driver behavior identification results and the vehicle handling and stability performance are significantly improved.

Integrated vehicle chassis control for active front steering and direct yaw moment control based on hierarchical structure

2018 ◽  
Vol 41 (9) ◽  
pp. 2428-2440 ◽  
Author(s):  
Jiaxu Zhang ◽  
Jing Li
Keyword(s):  
Sliding Mode ◽  
Null Space ◽  
Level Control ◽  
Direct Yaw Moment Control ◽  
Active Front Steering ◽  
Yaw Moment Control ◽  
Chassis Control ◽  
Moment Control ◽  
Vehicle Chassis ◽  
Yaw Moment

This paper presents an integrated vehicle chassis control (IVCC) strategy to improve vehicle handling and stability by coordinating active front steering (AFS) and direct yaw moment control (DYC) in a hierarchical way. In high-level control, the corrective yaw moment is calculated by the fast terminal sliding mode control (FTSMC) method, which may improve the transient response of the system, and a non-linear disturbance observer (NDO) is used to estimate and compensate for the model uncertainty and external disturbance to suppress the chattering of FTSMC. In low-level control, the null-space-based control reallocation method and inverse tyre model are utilized to transform the corrective yaw moment to the desired longitudinal slips and the steer angle increment of front wheels by considering the constraints of actuators and friction ellipse of each wheel. Finally, the performance of the proposed control strategy is verified through simulations of various manoeuvres based on vehicle dynamic software CarSim.

Simultaneous Model Order and Parameter Estimation (SMOPE) based on Random Asynchronous Particle Swarm Optimization

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 66-71
Author(s):  
Nor Azlina Ab. Aziz ◽  
Nor Hidayati Abd Aziz ◽  
Tasiransurini Ab Rahman ◽  
Norrima Mokhtar ◽  
Marizan Mubin
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
System Identification ◽  
Original Work ◽  
Particle Swarm ◽  
Identification Problems ◽  
Swarm Optimization ◽  
Model Order ◽  
Simultaneous Model ◽  
Slight Advantage

Simultaneous model order and parameter estimation (SMOPE) is a metaheuristic based system identification method. SMOPE was introduced using particle swarm optimization (PSO). There are several iteration strategies for PSO. The original work on SMOPE is based on synchronous PSO (S-PSO). However, in some works PSO using other iteration strategy is found to give better results. In this work, based on six system identification problems random asynchronous (RA-PSO) based SMOPE is found to have slight advantage over S-PSO.

scholarly journals Identification of Linear and Bilinear Systems: A Unified Study

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1790
Author(s):  
Jacob Benesty ◽  
Constantin Paleologu ◽  
Laura-Maria Dogariu ◽  
Silviu Ciochină
Keyword(s):  
System Identification ◽  
Wiener Filter ◽  
Low Rank ◽  
Identification Problems ◽  
Low Rank Approximation ◽  
Product Decomposition ◽  
Single Output ◽  
Unified Study ◽  
Recent Developments ◽  
Improved Performance

System identification problems are always challenging to address in applications that involve long impulse responses, especially in the framework of multichannel systems. In this context, the main goal of this review paper is to promote some recent developments that exploit decomposition-based approaches to multiple-input/single-output (MISO) system identification problems, which can be efficiently solved as combinations of low-dimension solutions. The basic idea is to reformulate such a high-dimension problem in the framework of bilinear forms, and to then take advantage of the Kronecker product decomposition and low-rank approximation of the spatiotemporal impulse response of the system. The validity of this approach is addressed in terms of the celebrated Wiener filter, by developing an iterative version with improved performance features (related to the accuracy and robustness of the solution). Simulation results support the main theoretical findings and indicate the appealing performance of these developments.

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