The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control Firstly, the vehicle dynamics model (ie, transfer function) is established according to the vehicle parameters Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a driver's expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the level of sensitivity to errors, i.e., low, middle, and high. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to driver's path-following characteristics, reducing the driver's workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in CarSim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed-speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, as compared with the classical control strategy of steering ratio, can improve task performance by about 7% and reduce the driver's physical workload and mental workload by about 35% and 50%, respectively, when following the given path.

Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics

Parametric optimization of novel electric–hydraulic hybrid steering system based on a shuffled particle swarm optimization algorithm

This paper presents a collaborative control system for an electric vehicle chassis based on a centralized and hierarchical control architecture. The centralized controller was designed for the suspension and steering system, which is used for improving ride comfort and handling stability; the hierarchical controller was designed for the braking system, which is used for distributing the proportion of hydraulic braking and regenerative braking to improve braking performance. These two sub-controllers function at the same level of the vehicle chassis control system. In order to reduce the potential conflict between the two sub-controllers and realize a coordination optimization of electric vehicle performance, a collaborative controller was built, which serves as the upper controller to carry out an overall coordination analysis according to vehicle signals and revises the decisions of sub-controllers. A simulation experiment was carried out with the MATLAB/Simulink software. The simulation results show that the proposed collaborative control system can achieve an optimized vehicle handling stability and braking safety.

Simulation Research on an Electric Vehicle Chassis System Based on a Collaborative Control System

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/0405.pdf

Model-based Rack Force Estimation for Electric Power Steering

Electric power steering (EPS) is more and more in use for passenger cars. Compared with hydraulic steering systems there are many advantages, such as reduced CO2 emissions and the possibility to use the EPS motor torque for advanced driver assistance systems. One task of the steering system is to give the driver an adequate steering feel. This includes providing road feedback and the right level of assistance torque. This article describes the steering torque control of a rack-assisted EPS. The controller's task is to follow a reference steering torque quickly and accurately. First, a mechanical model of the EPS is shown. Then, an H∞-controller is designed, implemented and compared with other steering torque controllers. As steering torque discontinuities are a topic when looking at new control algorithms, the phenomenon and its cause are analysed using a detailed mechanical model. The results of this analysis are considered in the controller design.

C. Dannöhl

Papers

H∞-control of a rack-assisted electric power steering system