Active force control with iterative learning control algorithm for a vehicle suspension

The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. ILC...

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Main Author:	Rosmazi, Rosli
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TL Motor vehicles Aeronautics Astronautics
Online Access:	http://umpir.ump.edu.my/id/eprint/9041/1/ROSMAZI%20BIN%20ROSLI.PDF
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id	my-ump-ir.9041
record_format	uketd_dc
spelling	my-ump-ir.90412021-08-19T04:17:20Z Active force control with iterative learning control algorithm for a vehicle suspension 2013-10 Rosmazi, Rosli TL Motor vehicles. Aeronautics. Astronautics The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. ILC algorithm is implemented into AFC-based control scheme to reduce its complexity and hence faster response, by replacing the use of artificial intelligence (Al) method as proposed by previous researcher. The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. The PID controller was first designed and tested prior to developing the AFC which was directly cascaded with the YIDdoop. A number of ILC algorithms were explicitly employed to compute the estimated mass in the AFC loop that is necessary to trigger the control action. The AFC with ILC (AFCIL) suspension system was experimented both through simulation and practical experimentation considering various ILC learning parameters, differenti operating conditions and a number of external disturbances to test and verify the system robustness. The simulation was conducted using MATLAB/Simulink software package whilstthe experimental study utilized the existing experimental rig with a hardware-in-the-loop simulation (HILS) configuration with the proposed ILC algorithms incorporated as the new research contribution. The results obtained, via various control schemes in, the form of PID, AFCIL and passive systems were rigorously, compared and analyzed.to ascertain the system performance in terms of its, ability, to improve riding comfort characteristics. The results imply that the proposed AFC-based scheme produces the best response with an approximately 50% improvement I in comparison to the 'PID and passive counterparts. 2013-10 Thesis http://umpir.ump.edu.my/id/eprint/9041/ http://umpir.ump.edu.my/id/eprint/9041/1/ROSMAZI%20BIN%20ROSLI.PDF application/pdf en public masters Universiti Teknologi Malaysia Faculty of Mechanical Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	TL Motor vehicles Aeronautics Astronautics
spellingShingle	TL Motor vehicles Aeronautics Astronautics Rosmazi, Rosli Active force control with iterative learning control algorithm for a vehicle suspension
description	The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. ILC algorithm is implemented into AFC-based control scheme to reduce its complexity and hence faster response, by replacing the use of artificial intelligence (Al) method as proposed by previous researcher. The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. The PID controller was first designed and tested prior to developing the AFC which was directly cascaded with the YIDdoop. A number of ILC algorithms were explicitly employed to compute the estimated mass in the AFC loop that is necessary to trigger the control action. The AFC with ILC (AFCIL) suspension system was experimented both through simulation and practical experimentation considering various ILC learning parameters, differenti operating conditions and a number of external disturbances to test and verify the system robustness. The simulation was conducted using MATLAB/Simulink software package whilstthe experimental study utilized the existing experimental rig with a hardware-in-the-loop simulation (HILS) configuration with the proposed ILC algorithms incorporated as the new research contribution. The results obtained, via various control schemes in, the form of PID, AFCIL and passive systems were rigorously, compared and analyzed.to ascertain the system performance in terms of its, ability, to improve riding comfort characteristics. The results imply that the proposed AFC-based scheme produces the best response with an approximately 50% improvement I in comparison to the 'PID and passive counterparts.
format	Thesis
qualification_level	Master's degree
author	Rosmazi, Rosli
author_facet	Rosmazi, Rosli
author_sort	Rosmazi, Rosli
title	Active force control with iterative learning control algorithm for a vehicle suspension
title_short	Active force control with iterative learning control algorithm for a vehicle suspension
title_full	Active force control with iterative learning control algorithm for a vehicle suspension
title_fullStr	Active force control with iterative learning control algorithm for a vehicle suspension
title_full_unstemmed	Active force control with iterative learning control algorithm for a vehicle suspension
title_sort	active force control with iterative learning control algorithm for a vehicle suspension
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Mechanical Engineering
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/9041/1/ROSMAZI%20BIN%20ROSLI.PDF
_version_	1783731949454491648

Active force control with iterative learning control algorithm for a vehicle suspension

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