scholarly journals Investigation on linear quadratic Gaussian control of semi-active suspension for three-axle vehicle

2020 ◽  
pp. 146134842096092
Author(s):  
Shaohua Li ◽  
Junwu Zhao ◽  
Zhida Zhang
Keyword(s):  
Active Suspension ◽  
Travel Speed ◽  
Vertical Vibration ◽  
Control Effect ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Analytic Hierarchy ◽  
Passive Suspension ◽  
On The Road ◽  
Weight Coefficients

An 8-DOF three-axle vehicle model with semi-active suspension is built in this paper, of which the accuracy is verified through simulations and experiments. Based on the optimal control theory, the linear quadratic Gaussian controller for semi-active suspension is designed with 10 evaluation indicators. Considering the deficiency of linear quadratic Gaussian control weight coefficients based on experience, analytic hierarchy process is employed to determine the weight coefficients of each indicator. The control effect is analyzed through MATLAB/Simulink. The adaptability of proposed control strategy under 25 driving conditions is analyzed with different road grades and speeds. The driving condition of “70 km/h travel speed on the road of grade B” is selected, under which the comparison of vehicle responses between semi-active suspension and passive suspension is made. Results show that the vertical vibration is effectively diminished by using semi-active suspension with linear quadratic Gaussian controller. Compared with passive suspension, the riding comfort is improved and the adverse effect on handling stability is eliminated. The three-axle vehicle with semi-active suspension has good adaptability to various working conditions.

Research on performance of vehicle semi-active suspension applied magnetorheological damper based on linear quadratic Gaussian control

2020 ◽  
Vol 51 (7-9) ◽  
pp. 119-126
Author(s):  
Shujing Sha ◽  
Zhongnan Wang ◽  
Haiping Du
Keyword(s):  
Active Suspension ◽  
Magnetorheological Damper ◽  
Optimal Feedback Control ◽  
Force Output ◽  
Linear Quadratic ◽  
Damping Force ◽  
Linear Quadratic Gaussian ◽  
Passive Suspension ◽  
Front Suspension ◽  
Suspension Model

With the development of automobile technology, the traditional passive suspension cannot meet people’s requirements for vehicle comfort and safety. For this reason, a variable damping semi-active suspension applied magnetorheological damper is proposed. By collecting various performance parameters of the front suspension, the optimal feedback control matrix is obtained by applying linear quadratic Gaussian control strategy, and the optimal damping force output is also obtained to improve comfort and vehicle safety by reducing vibration. The semi-active suspension model of a quarter vehicles was established by MATLAB/Simulink, and the simulation experiment was carried out. The results show that the semi-active suspension system with magnetorheological damper is superior to the traditional passive suspension in terms of vibration absorption; meanwhile, the root mean square values of vehicle acceleration, suspension dynamic deflection, tire dynamic travel, and tire dynamic load are reduced, which effectively improve the vehicle ride stability.

Dynamic monitoring feedback iteration control for damping-adjustable semi-active suspension systems

2021 ◽  
pp. 1-9
Author(s):  
Chongchong Li ◽  
Jiangyong Xiong ◽  
Tingshan Liu ◽  
Ziang Zhang
Keyword(s):  
Root Mean Square ◽  
Working Conditions ◽  
Control Algorithm ◽  
Active Suspension ◽  
Dynamic Monitoring ◽  
Iteration Algorithm ◽  
Mean Square ◽  
Control Effect ◽  
Analytic Hierarchy ◽  
Vehicle Simulation

In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering.

scholarly journals Design of LQG Controller for Active Suspension without Considering Road Input Signals

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Hui Pang ◽  
Ying Chen ◽  
JiaNan Chen ◽  
Xue Liu
Keyword(s):  
Angular Acceleration ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Vehicle Suspension ◽  
Linear Quadratic ◽  
The Road ◽  
Input Signals ◽  
Vehicle Suspension System ◽  
Weight Coefficients

As the road conditions are completely unknown in the design of a suspension controller, an improved linear quadratic and Gaussian distributed (LQG) controller is proposed for active suspension system without considering road input signals. The main purpose is to optimize the vehicle body acceleration, pitching angular acceleration, displacement of suspension system, and tire dynamic deflection comprehensively. Meanwhile, it will extend the applicability of the LQG controller. Firstly, the half-vehicle and road input mathematical models of an active suspension system are established, with the weight coefficients of each evaluating indicator optimized by using genetic algorithm (GA). Then, a simulation model is built in Matlab/Simulink environment. Finally, a comparison of simulation is conducted to illustrate that the proposed LQG controller can obtain the better comprehensive performance of vehicle suspension system and improve riding comfort and handling safety compared to the conventional one.

Stability Robustness of LQ and LQG Active Suspensions

1994 ◽  
Vol 116 (1) ◽  
pp. 123-131 ◽  
Author(s):  
A. G. Ulsoy ◽  
D. Hrovat ◽  
T. Tseng
Keyword(s):  
Controller Design ◽  
Active Suspension ◽  
Point Of View ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Active Suspensions ◽  
Quarter Car Model ◽  
Stability Robustness ◽  
Trade Offs ◽  
Two Degree Of Freedom

A two-degree-of-freedom quarter-car model is used as the basis for linear quadratic (LQ) and linear quadratic Gaussian (LQG) controller design for an active suspension. The LQ controller results in the best rms performance trade-offs (as defined by the performance index) between ride, handling and packaging requirements. In practice, however, all suspension states are not directly measured, and a Kalman filter can be introduced for state estimation to yield an LQG controller. This paper (i) quantifies the rms performance losses for LQG control as compared to LQ control, and (ii) compares the LQ and LQG active suspension designs from the point of view of stability robustness. The robustness of the LQ active suspensions is not necessarily good, and depends strongly on the design of a backup passive suspension in parallel with the active one. The robustness properties of the LQG active suspension controller are also investigated for several distinct measurement sets.

scholarly journals The Design of LQG Controller for Active Suspension Based on Analytic Hierarchy Process

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Lingjiang Chai ◽  
Tao Sun
Keyword(s):  
Analytic Hierarchy Process ◽  
Degrees Of Freedom ◽  
Active Suspension ◽  
Ride Quality ◽  
Linear Quadratic ◽  
Analytic Hierarchy ◽  
Performance Indexes ◽  
Suspension Control ◽  
Hierarchy Process ◽  
Selection Of

A full vehicle model with seven degrees of freedom based on active suspension control is established, and linear quadratic gaussian (LQG) is designed by applying optimal control theory. Especially, the methodology of Analytic Hierarchy Process (AHP) is used to make the selection of weighted coefficients of performance indexes, which can reduce ineffective job in contrast with experience method. From the simulation results, it is shown that ride quality of the vehicle with active suspension has been effectively improved in comparison with the vehicle of passive suspension by the methodology of AHP applying to the selection of the weights.

Study on Control Technology of Active Suspension Based on ADAMS and MATLAB

2014 ◽  
Vol 602-605 ◽  
pp. 1372-1377 ◽  
Author(s):  
Yi Zhang ◽  
Li Li Sun
Keyword(s):  
Pid Control ◽  
Ride Comfort ◽  
Active Suspension ◽  
Vertical Vibration ◽  
Vehicle Suspension ◽  
Control Effect ◽  
Vibration Acceleration ◽  
Suspension Control ◽  
Suspension Model ◽  
Control Principle

In order to improve the control effect of vehicle suspension, the simplified Seven-DOF active suspension model was created in ADAMS/View by applying the dynamics theory, and classical PID control principle was utilized to design an active suspension controller for vehicle. The vehicle model was imported into the PID controller established in MATLAB as a module to create a vehicle active suspension control model. According to the simulation results, compared with passive suspension, the PID control of active suspension can control effectively the vertical vibration acceleration (VVA),roll and pitch acceleration (RAA&PAA) of body ,which improved vehicle ride comfort performance.

Computation of passive suspension parameters for improvement of vehicle ride quality based on stochastic optimal controller with a look-ahead preview

2021 ◽  
pp. 095745652110003
Author(s):  
VSV Satyanarayana ◽  
LVV Gopala Rao ◽  
B Sateesh ◽  
N Mohan Rao
Keyword(s):  
Ride Comfort ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Ride Quality ◽  
Linear Quadratic ◽  
Passive Suspension ◽  
Optimum Parameters ◽  
Look Ahead ◽  
Performance Error ◽  
Suspension Performance

This article aims to determine the optimum parameters of a half-car model passive suspension vehicle passing on a random road. The optimum parameters are obtained based on the response of linear quadratic regulator control with a look-ahead preview for attaining the passive suspension performance nearly equivalent to the active suspension performance. The optimum parameters are estimated by equalizing mean square suspension controlling forces of passive and active vehicle models and subsequently minimizing the performance error between the two systems. The response of passive suspension with optimized parameters matches approximately with the active suspension response, with respect to ride comfort and road holding.

scholarly journals Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

2021 ◽  
Vol 11 (21) ◽  
pp. 10493
Author(s):  
Kun Wu ◽  
Jiang Liu ◽  
Min Li ◽  
Jianze Liu ◽  
Yushun Wang
Keyword(s):  
Clustering Algorithm ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Vehicle Model ◽  
Control Effect ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Suspension Control ◽  
Random Weighting ◽  
Weighting Coefficients

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.

scholarly journals OPTIMAL CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEM ON CARS

2020 ◽  
Vol 225 (13) ◽  
pp. 107-113
Author(s):  
Vũ Văn Tấn
Keyword(s):  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Optimal Controller ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian

Hệ thống treo là một trong những bộ phận quan trọng nhất trong thiết kế ô tô và là yếu tố quyết định đến sự thoải mái của lái xe, hành khách (độ êm dịu) và giữ được bám giữa lốp và mặt đường (độ an toàn). Bài báo này giới thiệu một mô hình ¼ ô tô có 2 bậc tự do sử dụng hệ thống treo chủ động với hai bộ điều khiển tối ưu: linear quadratic regulator và linear quadratic gaussian (linear quadratic regulator kết hợp với bộ quan sát Kalman-Bucy). Bằng cách sử dụng bộ quan sát Kalman-Bucy, số lượng cảm biến dùng để đo đạc các tín hiệu đầu vào của bộ điều khiển linear quadratic regulator đã được giảm thiểu tối đa chỉ còn các cảm biến thông thường như gia tốc của khối lượng được treo. Độ êm dịu và an toàn chuyển động khi ô tô sử dụng hệ thống treo chủ động được so sánh với ô tô sử dụng hệ thống treo bị động thông thường thông qua dịch chuyển của khối lượng được treo và gia tốc của nó. Kết quả mô phỏng đã thể hiện rõ giá trị sai lệch bình phương trung bình của gia tốc dịch chuyển thân xe với hệ thống treo tích cực điều khiển tối ưu linear quadratic regulator, linear quadratic gaussian đã giảm khoảng 20% so với ô tô sử dụng hệ thống treo bị động.

Vibration Control of Vehicle by Active Suspension with LQG Algorithm

2020 ◽  
Vol 17 (2) ◽  
pp. 8011-8018
Author(s):  
O. Gomonwattanapanich ◽  
N. Pannucharoenwong ◽  
P. Rattanadecho ◽  
S. Echaroj ◽  
S. Hemathulin
Keyword(s):  
Vertical Velocity ◽  
Pitch Angle ◽  
Active Suspension ◽  
Suspension System ◽  
Roll Angle ◽  
Vertical Position ◽  
Linear Quadratic ◽  
Passive Suspension ◽  
Roll Velocity ◽  
Passive Suspension System

In this paper, the ride performance of a vehicle with active suspension and Linear Quadratic Gaussian (LQG) controller has been studied and is compared to the performances of a traditional passive suspension system. The study includes variables that are related to a passenger’s comfort: vertical position, vertical velocity, pitch angle, pitch velocity, roll angle, and roll velocity. The performances of the two systems are evaluated by maximum values and root mean square (RMS) of the variables when riding on a sinusoidal road profile. The simulation results show that the vehicle with active suspension and LQG controller performs better than passive suspension system where the maximum values decrease by 85.77%, 92.73%, 50.31% 86.83%, 89.41%, 43.28%, and RMS values decrease by 88.59%, 92.36%, 42.99%, 87.61%, 90.85%, and 42.79% for vertical position, vertical velocity, pitch angle, pitch velocity, roll angle, and roll velocity, respectively.

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