scholarly journals System Optimization Algorithm for 3DOF Quarter Car Active Suspension

Author(s):  
M.K. Shehan ◽  
B.B. Sahari ◽  
N.A.B.A. Jalil ◽  
T.S. Hong ◽  
A.B. Asarry

This paper handles the synergy between the design and control optimization problem for an active car suspension system consisting both active and passive components. The dynamics of the suspension system are modeled utilizing a three degree of freedom (3DOF), linear with time invariant quarter car model with capability to capture the impact of the passive stiffness on suspension deflection depending up on the spectral density of road disturbances. Direct transcription, a strategy which guarantees system optimality, is presented and utilized to find the optimal design of the suspension system. The active system dynamics were analyzed with modified level of control force to examine how dynamic system should be designed accordingly when the active control force is introduced.

Author(s):  
Duval A. Johnson

This study is conducted to provide preliminary data that fractional calculus can be used to optimize active automobile suspension systems. Most automobile suspension systems perform their duties using a single spring with fixed damping rates and are referred to as being a passive system. An active suspension system has the ability to directly control force actuators in the suspension system or by varying the damping rates within the shock absorbers to provide control over body position, velocity, and acceleration. A mathematical model for a quarter car suspension system has been obtained to compare passive, integer, and fractionally controlled active suspension systems and show that fractional calculus may be used to improve the performance of any active system.


Author(s):  
Hosam K. Fathy ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy

The plant and control optimization problems are coupled in the sense that solving them sequentially does not guarantee system optimality. This paper extends previous studies of this coupling by relaxing their assumption of full state measurement availability. An original derivation of first-order necessary conditions for plant, observer, controller, and combined optimality furnishes coupling terms quantifying the underlying trilateral coupling. Special scenarios where the problems decouple are pinpointed, and a nested optimization strategy that guarantees system optimization strategy that guarantees system optimality is adopted otherwise. Applying these results to combined passive/active car suspension optimization produces a suspension design outperforming its passive, active, and sequentially optimized passive/active counterparts.


2014 ◽  
Vol 1036 ◽  
pp. 794-799 ◽  
Author(s):  
Łukasz Konieczny ◽  
Rafał Burdzik ◽  
Piotr Folęga

The paper presents results of investigation of car suspension system dynamics. In this research the multibody (Multi Body System - MBS) system software MSC.Adams was used. ADAMS software (MSC.Software) is a commercial software to build a multibody structural models. Modular design allows for the usage of applications with different focuses, such as rail, aviation and motor vehicles. Models with a large number of freedom degrees of the components are built with mass concentrated on the assumption that the system is composed of a rigid (or deformable) bodies combined in a specific way (connection spherical, sliding, rotary), moving under the action of the forces and moments of different types (concentrated or distributed forces, the forces of contact). The complex multibody systems are automatically generated by the Lagrange equations of motion of the second kind in absolute coordinates. Integral procedures used to solve the differential-algebraic equations include multistep algorithms with variable row and a variable-and fixed-step and one-step algorithms. The Adams/Car module enables building and simulation-based examination of individual car subsystems such as, for instance, the suspension, steering or driving system as well as their combinations forming a complete car. The programme contains an extensive library of structural solutions applied in cars. The geometry and relationship data of individual components are stored in libraries, and software operation on a standard user level can be brought down to defining positions of constraints in space. The software is compatible with various CAD programmes, thus enabling import of elements created in other applications. The study was conducted for the vehicle model of Fiat Seicento. The examined system of the complete vehicle consists of 49 kinematic degrees of freedom. The article examined the impact of chosen parameters on vehicle vibration in an Adams Car Ride. Used in simulation Adams/Car /Ride module allows to test vehicle dynamics forcing the position of the plate of test stand . Virtual model of the vehicle is set on four servo-motors. They can control any excitation combination of individual actuators (dispalcement and amplitude, phase between extortion, etc.) and determine all kinds of vibration (vertical, lateral, angular). The study was conducted for selected parameters of the test rig.


2016 ◽  
Vol 36 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Mahesh Nagarkar ◽  
G. J. Vikhe Patil

<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>


2017 ◽  
Vol 50 (1) ◽  
pp. 14519-14524 ◽  
Author(s):  
Sami Rajala ◽  
Tomi Roinila ◽  
Matti Vilkko ◽  
Oussama Ajala ◽  
Jochen Rauh

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