Evaluation of the transient response and implementation of a heading-angle controller for an autonomous ground vehicle

Abstract

In this paper, a dynamic mathematical model of an autonomous ground vehicle was used to analyse its transient response and to design a heading-angle controller for the vehicle. A suitable 'control-oriented model' that could accurately characterize the phenomenon of interest was used to design the controller. The efficacy of this model was evaluated by corroborating its results with experimental data. This model included the cornering stiffness of the tyres as an unknown parameter, and two approaches were attempted to estimate its value. The dynamics of the actuator were included in the analysis since the response time to steer the front wheel is of the same order as that of the heading-angle dynamics of the vehicle. The performance of two controllers (namely a classical transfer-function-based controller and an optimal linear quadratic regulator) were evaluated using the IPG: CarMaker Ò simulation platform over a range of speeds. The transfer-function-based controller was also implemented on the experimental test vehicle at low speeds (high-speed experimental implementation was not possible because of safety concerns). It was found that control gain scheduling helped to track the desired heading angles of the vehicle at various speeds. Subsequently, a lane-change manoeuvre using the test vehicle was performed to evaluate the controller further. It was found that the transfer-function-based headingangle controller could provide a comparable performance with that of the linear quadratic regulator, while keeping the sensing requirements to a minimum; thus, it was suitable for real-time implementation in an autonomous ground vehicle.

Frequency domain modeling, analysis and verification of electro-hydraulic servo steering system for heavy vehicles

“…By inputting a sinusoidal signal to the system and comparing the input signal with the output signal of the system, sinusoidal sweep could obtain the amplitude ratio and phase difference of sinusoids signal in different frequencies. 20,21…”

“…In order to verify linearization analysis method and linear frequency domain model, two comparison verification schemes are designed: By inputting a sinusoidal signal to the system and comparing the input signal with the output signal of the system, sinusoidal sweep could obtain the amplitude ratio and phase difference of sinusoids signal in different frequencies. 20,21 According to the amplitude ratio and phase difference, the amplitude-frequency phase-frequency characteristic curves of the system, that is, the Bode plots, can be obtained. The SFRA process diagram is shown in Figure 10.…”

DGPR‐MPC: Learning‐based model predictive controller for autonomous vehicle path following

“…Sahoo et al. [2] modelled the vehicle using the 2DOF model, analyzed the transient response, and designed the steering controller using the 2DOF model. Segel et al.…”

“…In the middle of the 20th century, the linear two-degrees-of-freedom (2DOF) steering model was first investigated. Sahoo et al [2] modelled the vehicle using the 2DOF model, analyzed the transient response, and designed the steering controller using the 2DOF model. Segel et al [3] proposed a three-degrees-of-freedom (3DOF) steering model with three degrees of freedom.…”

Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

“…1 Benefit from the unmanned ground vehicles, driving tasks can be conducted by the automated driving system, which greatly reduces the drivers’ workload and improves the driving efficiency and safety. 2 However, in the real and complex driving environment, the static and moving obstacles largely impede the implementation and usage of unmanned ground vehicles. 3 In the presence of static and moving obstacles, collision-free paths need to be planned and tracked to achieve driving tasks and guarantee driving safety.…”