Linear Quadratic Regulator and Fuzzy controller application in full-car model of suspension system with Magnetorheological shock absorber

2010 ◽  
Author(s):  
Ali Fellah Jahromi ◽  
A. Zabihollah
Keyword(s):  
Shock Absorber ◽  
Fuzzy Controller ◽  
Linear Quadratic Regulator ◽  
Suspension System ◽  
Linear Quadratic ◽  
Car Model

Semi Active Vibration Control of a Passenger Car Using Magnetorheological Shock Absorber

2010 ◽  
Author(s):  
Ali Fellah Jahromi ◽  
A. Zabihollah
Keyword(s):  
Vibration Control ◽  
Control Algorithm ◽  
Shock Absorber ◽  
Active Vibration Control ◽  
Linear Quadratic Regulator ◽  
Suspension System ◽  
Linear Quadratic ◽  
Passenger Car ◽  
Road Profile ◽  
Active Vibration

A novel semi-active control system for suspension systems of passenger car using Magnetorheological (MR) damper is introduced. The suspension system is considered as a massspring model with an eight-degrees-of-freedom, a passive damper and an active damper. The semi-active vibration control is designed to reduce the amplitude of automotive vibration caused by the alteration of road profile. The control mechanism is designed based on the optimal control algorithm, Linear Quadratic Regulator (LQR). In this system, the damping coefficient of the shock absorber changes actively trough inducing magnetic field. It is observed that utilizing the present control algorithm may significantly reduce the vibration response of the passenger car, thus, providing comfortable drive. The new developed suspension system may lead to design and manufacturing of passenger car in which the passenger may not feel the changes in road profile from highly bumpy to smooth profile.

scholarly journals Quarter car model optimization of active suspension system using fuzzy PID and linear quadratic regulator controllers

2021 ◽  
Vol 6 (3) ◽  
pp. 088-097
Author(s):  
Abdussalam Ali Ahmed
Keyword(s):  
Control System ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Fuzzy Pid ◽  
Linear Quadratic ◽  
Fuzzy Pid Control ◽  
Quarter Car Model ◽  
Car Model ◽  
Quarter Car

The primary objective of this paper is to improve the performance of a car's active suspension system and control the vibrations that occurred in the car's using two well-known control technologies, namely the Linear Quadratic Regulator (LQR) and fuzzy PID control. When the car suspension is designed, a quarter car model with two degrees of freedom is used. A complete control system is needed to provide the desired suspension performance and characteristics such as passenger comfort, road handling, and suspension deflection, this control system performed using the MATLAB/SIMULINK and includes three parts: input signals (actuator force and road profile), Controller part, and the suspension system model. The simulation results from the implemented Simulink models show a comparison between the uncontrolled suspension system and the suspension system with a fuzzy PID controller and the active suspension system of the car based on the linear-quadratic regulator, and it is explained thoroughly.

Improvement on Ride Comfort of Quarter-Car Active Suspension System Using Linear Quadratic Regulator

2018 ◽  
pp. 431-441
Author(s):  
Sharifah Munawwarah Syed Mohd Putra ◽  
Fitri Yakub ◽  
Mohamed Sukri Mat Ali ◽  
Noor Fawazi Mohd Noor Rudin ◽  
Zainudin A. Rasid ◽  
...  
Keyword(s):  
Ride Comfort ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Linear Quadratic ◽  
Quarter Car

scholarly journals Online Reinforcement Learning-Based Control of an Active Suspension System Using the Actor Critic Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 8060
Author(s):  
Ahmad Fares ◽  
Ahmad Bani Younes
Keyword(s):  
Reinforcement Learning ◽  
Least Squares Method ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Recursive Least Squares ◽  
Linear Quadratic ◽  
Reward Function ◽  
Road Profile ◽  
The One

In this paper, a controller learns to adaptively control an active suspension system using reinforcement learning without prior knowledge of the environment. The Temporal Difference (TD) advantage actor critic algorithm is used with the appropriate reward function. The actor produces the actions, and the critic criticizes the actions taken based on the new state of the system. During the training process, a simple and uniform road profile is used while maintaining constant system parameters. The controller is tested using two road profiles: the first one is similar to the one used during the training, while the other one is bumpy with an extended range. The performance of the controller is compared with the Linear Quadratic Regulator (LQR) and optimum Proportional-Integral-Derivative (PID), and the adaptiveness is tested by estimating some of the system’s parameters using the Recursive Least Squares method (RLS). The results show that the controller outperforms the LQR in terms of the lower overshoot and the PID in terms of reducing the acceleration.

scholarly journals Optimization of the linear quadratic regulator (LQR) control quarter car suspension system using genetic algorithm

2016 ◽  
Vol 36 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Mahesh Nagarkar ◽  
G. J. Vikhe Patil
Keyword(s):  
Genetic Algorithm ◽  
Linear Quadratic Regulator ◽  
Suspension System ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Linear Quadratic ◽  
Multi Objective ◽  
Weighting Matrix ◽  
Car Suspension ◽  
Quarter Car

<p>In this paper, a genetic algorithm (GA) based in an optimization approach is presented in order to search the optimum weighting matrix parameters of a linear quadratic regulator (LQR). A Macpherson strut quarter car suspension system is implemented for ride control application. Initially, the GA is implemented with the objective of minimizing root mean square (RMS) controller force. For single objective optimization, RMS controller force is reduced by 20.42% with slight increase in RMS sprung mass acceleration. Trade-off is observed between controller force and sprung mass acceleration. Further, an analysis is extended to multi-objective optimization with objectives such as minimization of RMS controller force and RMS sprung mass acceleration and minimization of RMS controller force, RMS sprung mass acceleration and suspension space deflection. For multi-objective optimization, Pareto-front gives flexibility in order to choose the optimum solution as per designer’s need.</p>

Fuzzy Control and Co-Simulation of Automobile Semi-Active Suspension System Based on SIMPACK and MATLAB

2010 ◽  
Vol 39 ◽  
pp. 50-54 ◽  
Author(s):  
Shao Yi Bei ◽  
Jing Bo Zhao ◽  
Lan Chun Zhang ◽  
Shao Hua Liu
Keyword(s):  
Ride Comfort ◽  
Fuzzy Controller ◽  
Active Suspension ◽  
Simulation Software ◽  
Suspension System ◽  
The Body ◽  
Vertical Acceleration ◽  
Passive Suspension ◽  
Pulse Input ◽  
Car Model

Using the multi-body simulation software SIMPACK as platform, a whole CHANGHE mini-car model was built. A fuzzy controller was adopted based on MATLAB/SIMULINK software to control the full car model. Pulse input running test simulation was carried out under co-simulation of SIMAT. The results showed that compared to passive suspension, with the speed 40km/h, the body vertical acceleration, body pitch angular velocity, standard deviation and peak were respectively decreased by 10.76%, 18.03% and 20.48%, 12.13%. The semi-active suspension system with fuzzy controller had better performance than passive suspension, reduced vibration effectively and improved automotive ride comfort.

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