Vehicle State Estimation for Rollover Avoidance

2008 ◽  
Author(s):  
Jihua Huang
Keyword(s):  
State Estimation ◽  
Least Square ◽  
Lateral Acceleration ◽  
Extreme Condition ◽  
Recursive Least Square ◽  
Nonlinear Region ◽  
Main Challenge ◽  
Roll Velocity ◽  
Vehicle State Estimation ◽  
And Control

To enhance vehicle/road safety, rollover warning and control systems have received considerable research interest in recent years, especially for vehicles with high center of gravity (CG). Accurate and reliable estimates of the relevant vehicle states facilitate the design of such systems. This paper investigates the state estimation for rollover avoidance, in which the relevant states include vehicle roll velocity and roll angle, as well as sideslip velocity and yaw velocity. The main challenge of the design comes from the fact that, under near-rollover situations, vehicle dynamics is complex and nonlinear. Not only vehicle suspension and tires are in their nonlinear region, but also vehicle yaw, sideslip and roll motions are highly coupled. In addition, the estimation needs to deal with sensor biases and sensor nonlinearity under this extreme condition. To address those issues, this paper proposes a vehicle state estimation design that consists of three parts: a sensor pre-filter, an Extended Kalman filter (EKF), and a sideslip velocity estimator. The sensor pre-processor removes sensor biases by utilizing the Recursive Least Square technique with a varying forgetting factor. The EKF is designed based on a linear yaw/sideslip/roll model, and its feedback gains are further scheduled based on vehicle lateral acceleration in order to reduce the effects of increased model inaccuracy as vehicle roll motion becomes more severe. The sideslip velocity estimator adjusts the sideslip velocity estimated by the EKF to extend the estimation to the nonlinear region. Both simulation and vehicle fishhook testing have been used to verify the effectiveness of the design.

Model Learning in a Multistage Machining Process: Online Identification of Force Coefficients and Model Use in the Manufacturing Enterprise

2013 ◽  
Author(s):  
Parikshit Mehta ◽  
Laine Mears
Keyword(s):  
Process Control ◽  
Least Square ◽  
Machining Process ◽  
Force Model ◽  
Recursive Least Square ◽  
Force Coefficient ◽  
Model Learning ◽  
Force Coefficients ◽  
Control Objective ◽  
And Control

This work presents a systems approach in machining process control. Traditional force-based machining process control has been focused on single machine-single operation. The force or power sensor is used to measure the instantaneous force/power, and control action is taken by changing the feedrate in real time to follow a given force setpoint. The application of such control has successfully been implemented to prevent chatter and to elongate tool life by minimizing tool wear. This research seeks to extend the application of control algorithms to learn about the machining system (comprised in this context of a workpiece being operated on in progressive machining), and how knowledge generated by the process can be passed on to the next process for optimization. To demonstrate this, turning of a partially hardened bar is explored. A nonlinear mechanistic force model-based control framework attempts to control the cutting force at a designated setpoint, with material properties changing over the cut. The force coefficients for the material are calculated offline using experimental data and Bayesian inference methods. Since the hardened part of the bar will shift the force coefficient values, an online estimation strategy (Bayesian Recursive Least Square estimator) is used to learn the new coefficients as well as satisfying the control objective. With the newly learned coefficients passed downstream, the subsequent operation experiences no compromise of control objective as well reduces the maximum values of force encountered. Numerical analyses presented show the adaptation and control scheme performance.

Non-Linear Complementary SO(3) Filters for Attitude Estimation and Navigation of Ground Vehicles

2014 ◽  
Vol 663 ◽  
pp. 254-258
Author(s):  
Fargham Sandhu ◽  
Hazlina Selamat ◽  
Yahaya Md Sam
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Least Square ◽  
Low Grade ◽  
Ground Vehicles ◽  
Recursive Least Square ◽  
Stability And Control ◽  
Filter Approach ◽  
Navigational System ◽  
And Control

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.

scholarly journals Recursive least square and control for PUMA robotics

2021 ◽  
Vol 21 (2) ◽  
pp. 1238
Author(s):  
Lafta E. Jumaa Alkurawy
Keyword(s):  
Real Time ◽  
Inverse Kinematics ◽  
Algebraic Method ◽  
Pi Controller ◽  
Least Square ◽  
Recursive Least Square ◽  
End Effector ◽  
And Control ◽  
Reference Input

<p>The solution of inverse kinematics system based on recursive least square (RLS) theorem is improved this paper. The task in joints of robotics is inverse kinematics for PUMA robotics. The design the manipulator of robotics is not simple if due to model of algebraic method. I suggested a method of RLS method to get predicts the positions of robot and it is comfortable the applications in real-time.<strong> </strong>The RLS is used to find the solution of the inverse kinematics for the joints 6-dof of the robotics. This technique is important to compute the joints of each arm space with Cartesian axes in the end-effector. The identification will be in each joint for PUMA by RLS and applied PI controller on each joint to get the response follows the reference input by tuning the values of coefficients of PI.</p>

An Implicit Quadratic H∞ Self-Tuning Controller

1991 ◽  
Vol 113 (4) ◽  
pp. 729-735 ◽  
Author(s):  
R. A. Hashim ◽  
M. J. Grimble
Keyword(s):  
Diophantine Equations ◽  
Simple Procedure ◽  
Optimal Solution ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Control Signals ◽  
Control Scheme ◽  
Self Tuning ◽  
And Control

An implicit H∞ self-tuning control scheme is presented. Costing of the system error and control signals is achieved using a dynamic cost function. The H∞ optimal solution to this problem is obtained using a recursive least square identification algorithm. The simple procedure for calculating the controller, without solving any diophantine equations, make this method particularly suitable for self-tuning control applications.

scholarly journals On co-estimation and validation of vehicle driving states by a UKF-based approach

2021 ◽  
Vol 12 (1) ◽  
pp. 19-30
Author(s):  
Peng Wang ◽  
Hui Pang ◽  
Zijun Xu ◽  
Jiamin Jin
Keyword(s):  
State Estimation ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
Control Plant ◽  
Estimation Method ◽  
Lateral Acceleration ◽  
Accurate Information ◽  
Sideslip Angle ◽  
Safety Control ◽  
Vehicle State Estimation

Abstract. It is necessary to acquire the accurate information of vehicle driving states for the implementation of automobile active safety control. To this end, this paper proposes an effective co-estimation method based on an unscented Kalman filter (UKF) algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. First, a 3 degrees-of-freedom (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of the front steer angle and longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation is realized in the MATLAB/Simulink environment by using the pseudo-measured values and UKF algorithm concurrently. The results show that the proposed UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method.

Kernel Recursive Least Square Approach for Power System Harmonic Estimation

2021 ◽  
pp. 1-16
Author(s):  
Omar Avalos ◽  
Erik Cuevas ◽  
Héctor G. Becerra ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
...  
Keyword(s):  
Power System ◽  
Least Square ◽  
Recursive Least Square ◽  
Least Square Approach
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