TAILIEUCHUNG - Optimal control of vehicle active suspension system

A comparison between passive and active suspension system using PID and LQR control technique with road disturbance as input has been made. Simulation study has been made on the active suspension system using MATLAB/SIMULINK. | Journal of Automation and Control Engineering Vol. 6, No. 1, June 2018 Optimal Control of Vehicle Active Suspension System Vivek Kumar Maurya and Narinder Singh Bhangal Instrumentation and Control Engg., National Institute of Technology,Jalandhar,India Email: vivek21989@, nsbhangal@ reduce the stability of vehicle and if someone wants a hard suspension, then it can reduce the effect of external forces, resulting in reduced suspension but increased body vibration[1]. To overcome this problem we can use either semi-active or active suspension system. Semiactive suspension system contain a variable and controllable damper rather than fixed damper as in case of passive suspension system. On the other hand, active suspension system capable of adjusting its parameter values itself by using an actuator parallel to the spring and damper to provide better results. Active suspension system is a closed loop system, with a controller in loop to calculate how much energy is dissipated or absorbed by actuator. A lot has been reported in literature on the control strategies of the active suspension system. Linear quadratic regulator and fuzzy logic controllers are the popular controller used to improve the ride comfort and road handling. A comparison between passive and active suspension system was performed by using different types of road profiles for quarter car model, in which LQR control is found to be better in suppressing the vibrations, than passive system[2][3].LQR and PID control techniques has successfully implemented to the linear active suspension system for a quarter car model. LQR control performs better than PID as far as ride comfort is concerned [4][5][6].A performance comparison between LQR,H∞ and passive suspension system have been made, in which the results show that LQR control shows a better performance over the H∞ control strategy[7][8][9]. The aim of the paper is to design a linear quadratic regulator controller to improve

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