scholarly journals Evaluation of robust steering control law by combination of model matching and optimal observer by the evaluation tool using liner bicycle model

2017 ◽  
Vol 83 (848) ◽  
pp. 16-00517-16-00517 ◽  
Author(s):  
Kenji TAKAHATA ◽  
Morio TAKAHAMA ◽  
Koichi OKAMURA ◽  
Toshio OHTA
Keyword(s):  
Control Law ◽  
Model Matching ◽  
Steering Control ◽  
Evaluation Tool ◽  
Bicycle Model

Three-Dimensional Curve Tracking for Particles Using Gyroscopic Control

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Chuanfeng Wang
Keyword(s):  
Tracking Control ◽  
Three Dimensional ◽  
Smooth Curve ◽  
Autonomous Agent ◽  
Control Law ◽  
Steering Control ◽  
3D Space ◽  
Moving Direction ◽  
Tangent Direction ◽  
Curve Tracking

Curve-tracking control is challenging and fundamental in many robotic applications for an autonomous agent to follow a desired path. In this paper, we consider a particle, representing a fully actuated autonomous robot, moving at unit speed under steering control in the three-dimensional (3D) space. We develop a feedback control law that enables the particle to track any smooth curve in the 3D space. Representing the 3D curve in the natural Frenet frame, we construct the control law under which the moving direction of the particle will be aligned with the tangent direction of the desired curve and the distance between the particle and the desired curve will converge to zero. We demonstrate the effectiveness of the proposed 3D curve-tracking control law in simulations.

Vehicle yaw-inertia- and mass-independent adaptive steering control

2009 ◽  
Vol 223 (9) ◽  
pp. 1101-1108 ◽  
Author(s):  
J Wang ◽  
M F Hsieh
Keyword(s):  
Adaptive Control ◽  
Tracking Error ◽  
Stability Control ◽  
Driving Safety ◽  
Control Law ◽  
Steering Control ◽  
Tracking Accuracy ◽  
Unknown Parameters ◽  
Practical Applications ◽  
Adaptive Control Law

This paper describes a vehicle stability control (VSC) system using a vehicle yaw-inertia- and mass-independent adaptive control law. As a primary vehicle active control system, VSC can significantly improve vehicle driving safety for passenger cars and enhance trajectory tracking accuracy for other applications such as autonomous, surveillance, and mobile robot vehicles. For the designs of vehicle dynamic control systems, vehicle yaw inertia and mass are two of the most important parameters. However, in practical applications, vehicle yaw inertia and mass often change with vehicle payload and load distribution. In this paper, an adaptive control law is proposed to treat the vehicle yaw inertia and mass as unknown parameters and automatically address their variations. For the proposed adaptive control law, asymptotic stability of the yaw rate tracking error was proved by a Lyapunov-like analysis for certain vehicle architectures under some reasonable assumptions. The performance of the yaw-inertia- and mass-independent adaptive VSC system was evaluated under several driving conditions (i.e. double lane changing on a slippery surface and braking on a split- μ surface tests) through simulation studies using a high-fidelity full-vehicle model provided by CarSim®.

Application of Robust Control Law for Linear Slider

2008 ◽  
Vol 2 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Satoshi Yamamoto ◽  
◽  
Shinji Wakui
Keyword(s):  
Robust Control ◽  
Disturbance Observer ◽  
High Speed ◽  
Control Law ◽  
Model Matching ◽  
Positioning Accuracy ◽  
Control Laws ◽  
Modified Model ◽  
Applied Model ◽  
Model Following

The most important specifications in precision positioning are positioning accuracy and settling time. When a positioning sensor based on required specifications is installed in equipment, every effort is made to realize throughput, i.e., high-speed positioning. We applied model-matching 2-degree-of-freedom (DOF) control to a linear slider to realize positioning, but found that positioning waveforms were uneven under different conditions. To overcome this problem, we applied robust control laws, e.g., MM-2DOF with a disturbance observer, robust 2DOF control, and modelfollowing 2DOF control to the linear slider. A comparative study confirmed experimentally that robust 2DOF control was most suitable. To improve modelfollowing 2DOF control, we modified model-following 2DOF control and its robust positioning.

Modeling and Trajectory Control of a Forklift-Like Wheeled Robot

2014 ◽  
Author(s):  
Ho-Hoon Lee
Keyword(s):  
Asymptotic Stability ◽  
Real Time ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Trajectory Control ◽  
Control Law ◽  
Steering Control ◽  
Control Laws ◽  
Control Scheme ◽  
Trajectory Generator

In this paper, kinematic and dynamic models are derived for a forklift-like four-wheeled mobile robot, and then, based on the models, a new trajectory control scheme is designed and evaluated for the robot. The dynamic model, exhibiting non-minimum-phase characteristics, is derived by applying Lagrange’s equations and then the control law is design by using Lyapunov stability theorem and the loop shaping method. The proposed control scheme consists of a trajectory generator, a motion control law, and a steering control law. First, a real-time trajectory generator is designed based on the nonholonomic kinematic constraints of the robot, in which the reference driving speed and time rate of heading angle are computed in real time for a given desired trajectory of the robot. The proposed trajectory generator guarantees a local asymptotic stability. Next, motion and steering control laws are designed based on the dynamic model of the robot. The motion and steering control laws are used to control the robot speed and steering angle. The proposed control guarantees asymptotic stability of the trajectory control while keeping all internal signals bounded. Finally, the validity of the proposed control scheme is shown by realistic computer simulations with one sampling-time delay in the control loop.

scholarly journals Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Jinghua Guo ◽  
Keqiang Li ◽  
Jingjing Fan ◽  
Yugong Luo ◽  
Jingyao Wang
Keyword(s):  
Electric Vehicles ◽  
Sliding Mode ◽  
Variable Structure ◽  
Control Law ◽  
Time Varying ◽  
Steering Control ◽  
Structure Control ◽  
Adaptive Sliding Mode ◽  
Automatic Steering ◽  
Neural Fuzzy

AbstractThis paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Sign in / Sign up

Export Citation Format

Share Document