Optimal Preview Semiactive Suspension

1996 ◽  
Vol 118 (1) ◽  
pp. 99-105 ◽  
Author(s):  
L. Jezequel ◽  
V. Roberti
Keyword(s):  
Degrees Of Freedom ◽  
Active Suspension ◽  
Control Law ◽  
Semiactive Control ◽  
Active System ◽  
Two Degrees Of Freedom ◽  
Suspension Control ◽  
Computer Controlled ◽  
Track Irregularities ◽  
Semiactive Suspension

This paper examines an optimal preview semiactive suspension of a quarter-coach model moving along randomly profiled track. This optimal computer-controlled suspension is designed only to dissipate energy, and is able to use knowledge of track irregularities over a distance L in front of the train. Thus the deformation of the track can be taken into account when calculating the semi-active suspension control law. First, the expression of the optimal preview semiactive control law is established. Then, using a two-degrees-of-freedom quarter-coach model, preview information is shown to improve the behavior of an optimal non-preview semi-active system, which can come close to the performance of an active system.

scholarly journals Design of the optimal control for the active suspension

2021 ◽  
Vol 31 ◽  
pp. 1-6
Author(s):  
Sorin MARCU ◽  
◽  
Dinel POPA ◽  
Nicolae-Doru STANESCU ◽  
Nicolae PANDREA
Keyword(s):  
Optimal Control ◽  
Degrees Of Freedom ◽  
Active Suspension ◽  
Suspension System ◽  
Vertical Acceleration ◽  
Passive System ◽  
Active System ◽  
Matlab Simulink ◽  
Two Degrees Of Freedom ◽  
Lqr Control

The main purpose of the suspension is to minimize vertical acceleration. Through this paper we aim to analyze two PID and LQR control techniquesto reduce system vibrations. The active system will be compared to a passive system using two types of profile. Matlab / Simulink software is used to evaluate the performance of the two controllers using a system with two degrees of freedom. The analysis shows that we can control the suspension system using the two techniques to improve the comfort and safety of the vehicle.

Stochastic Optimal Control of Vehicles With Elastic Body and Active Suspension

1986 ◽  
Vol 108 (2) ◽  
pp. 106-110 ◽  
Author(s):  
Aleksander Hac´
Keyword(s):  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Measurement Errors ◽  
Active Suspension ◽  
Active System ◽  
State Behavior ◽  
Controlled System ◽  
Sensor Arrangement ◽  
Suspension Control ◽  
Control Forces

A discrete-continuous vibrating system, which can be treated as a model of a vehicle with an active suspension moving on a randomly profiled road, is considered in this paper. By the use of stochastic optimal control and estimation theory the suspension control forces and the steady-state behavior of an optimally controlled system in the presence of measurement errors are calculated and compared with the performance of an optimal passive system. The emphasis is on modeling and measurement problems. The need for taking body elasticity into account in the vehicle model is considered and the influence of sensor arrangement and accuracy upon the performance of the active system is determined.

Active Suspension Control for an Off-Road Vehicle

1987 ◽  
Vol 201 (1) ◽  
pp. 1-10 ◽  
Author(s):  
D A Crolla ◽  
D N L Horton ◽  
R H Pitcher ◽  
J A Lines
Keyword(s):  
Attitude Control ◽  
Ride Comfort ◽  
Active Suspension ◽  
Active System ◽  
Road Vehicle ◽  
Suspension Systems ◽  
Body Attitude ◽  
Active Suspension Systems ◽  
Suspension Control ◽  
Recent Developments

After a review of recent developments in active suspension systems, a semi-active system fitted to an off-road vehicle is described. Theoretically predicted results are presented alongside data measured on the actual vehicle. The benefits of the semi-active system over a passive suspension are improved ride comfort and improved body attitude control.

Design of an Active Suspension Control for a Four DOF Model Using Genetic Algorithm

2005 ◽  
Author(s):  
S. Gosselin-Brisson ◽  
M. Bouazara ◽  
M. J. Richard
Keyword(s):  
Genetic Algorithm ◽  
Degrees Of Freedom ◽  
Real Life ◽  
Active Suspension ◽  
Performance Criterion ◽  
Control Methods ◽  
Frequency Sensitivity ◽  
Suspension Control ◽  
Passive Suspension System ◽  
Real Vehicle

This paper presents the design of an active suspension controller for an automotive vehicle. A four degrees of freedom linear model is used to represent a vehicle with different front and rear characteristics. Filtered road and acceleration inputs are applied to the model to simulate real life use. The performance criterion are filtered to include frequency sensitivity and weighted based on a standard passive suspension system. Independent front and rear controllers are optimised with the genetic algorithm. The controller includes linear gains and frequency dependency to take advantage of these two different control methods. The number of sensors and the order of the filters are limited to facilitate implementation on a real vehicle.

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.

Online Adaptive Neuro-Fuzzy Based Full Car Suspension Control Strategy

2015 ◽  
pp. 992-1039
Author(s):  
Laiq Khan ◽  
Shahid Qamar
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Sliding Mode ◽  
Active Suspension ◽  
Suspension System ◽  
Car Model ◽  
Car Suspension ◽  
Neuro Fuzzy ◽  
Suspension Control ◽  
Soft Computing Technique

Suspension system of a vehicle is used to minimize the effect of different road disturbances for ride comfort and improvement of vehicle control. A passive suspension system responds only to the deflection of the strut. The main objective of this work is to design an efficient active suspension control for a full car model with 8-Degrees Of Freedom (DOF) using adaptive soft-computing technique. So, in this study, an Adaptive Neuro-Fuzzy based Sliding Mode Control (ANFSMC) strategy is used for full car active suspension control to improve the ride comfort and vehicle stability. The detailed mathematical model of ANFSMC has been developed and successfully applied to a full car model. The robustness of the presented ANFSMC has been proved on the basis of different performance indices. The analysis of MATLAB/SMULINK based simulation results reveals that the proposed ANFSMC has better ride comfort and vehicle handling as compared to Adaptive PID (APID), Adaptive Mamdani Fuzzy Logic (AMFL), passive, and semi-active suspension systems. The performance of the active suspension has been optimized in terms of displacement of seat, heave, pitch, and roll.

Active Suspension Control Synthesis Using Reduced Order Models That Account for the Time Delay Between Road Inputs

2006 ◽  
Author(s):  
R. Michael Van Auken
Keyword(s):  
Time Delay ◽  
Model Reduction ◽  
Active Suspension ◽  
High Order ◽  
Control Algorithms ◽  
Control Law ◽  
Order Model ◽  
Ground Vehicle ◽  
Reduced Order ◽  
Suspension Control

The control of wheeled ground vehicle suspension systems is well suited for analysis and refinement using multi-input multi-output (MIMO) control law synthesis methods for linear systems. Usually it is necessary and desirable to develop the control algorithms using a reduced order model of the system. Since such vehicles are also characterized by correlated road inputs with time delay between the front and rear wheels, it is also desirable to consider this delay during the model reduction process. If this delay is taken into consideration, then it may be possible to develop low order control algorithms which compensate for the vehicle modes that are disturbed by the road inputs, resulting in improved overall performance. This paper describes the application of model reduction to a model of a ground vehicle for active suspension control law synthesis. The vehicle is described by a high order MIMO model of a “half-car” with four rigid-body degrees of freedom and flexible body modes to account for structural vibration, plus additional states to represent colored noise road disturbance inputs. Fourth order MIMO models suitable for control law synthesis are then determined using internal balancing, taking into consideration the time delay between the front and rear wheels, followed by subsystem elimination. The performance of the vehicle (high order model) with the resulting low order active suspension control laws is then assessed.

The Hexapodopter: A Hybrid Flying Hexapod—Holonomic Flying Analysis

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Daniel Soto-Guerrero ◽  
José Gabriel Ramírez-Torres
Keyword(s):  
Degrees Of Freedom ◽  
The Body ◽  
Design Criteria ◽  
Control Law ◽  
Two Degrees Of Freedom ◽  
Main Design ◽  
Walking Machine ◽  
Six Degrees Of Freedom ◽  
Thrust Vector ◽  
Thrust Forces

This document introduces the holonomic flying capabilities of the Hexapodopter, a six-legged walking machine capable of vertical take-off and landing. For ground locomotion, each limb has two degrees-of-freedom (2DoF); while the thrust required for flying is provided by six motors mounted close to every knee, so the thrust vector can be reoriented depending on the configuration of each limb. The capacity of reorienting the thrust forces makes the Hexapodopter a true holonomic vehicle, capable of individually controlling its six degrees-of-freedom (6DoF) on the air without reorienting any of the thrust motors nor the body. The main design criteria and validation will be discussed on this paper, as well as a control law for the vehicle.

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