A New Model for MacPherson Suspension System and Observation of Ride Characteristics and Its Optimal Control

2005 ◽  
Author(s):  
M. S. Fallah ◽  
S. Fardisi ◽  
M. Eghtesad
Keyword(s):  
Optimal Control ◽  
Angular Displacement ◽  
Suspension System ◽  
Control System Design ◽  
Control Law ◽  
Vertical Acceleration ◽  
Inertia Moment ◽  
New Model ◽  
Camber Angle ◽  
Unsprung Mass

In this paper a new model for the MacPherson suspension system and its optimal control are investigated. The focuses of the modeling were to add the rotational motion of the unsprung mass and considering physical characteristics of the spindle such as mass and inertia moment. The vertical acceleration of the sprung mass is measured, while the angular displacement of the control arm is estimated. According to this model the ride characteristics such as alterations of the camber angle, king-pin angle and track are displayed. This model is more general in the sense that it provides an extra degree of freedom in determining the plant model for control system design. Optimal control theory was employed to derive a control law for an active suspension system. The performance degradation with an active actuator is evaluated. Simulations are also provided.

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.

Modified Skyhook Control for the Semi-Active Macpherson Strut Suspension: A New Model and HILS

2000 ◽  
Author(s):  
Keum-Shik Hong ◽  
Hyun-Chul Sohn ◽  
J. Karl Hedrick
Keyword(s):  
Relative Velocity ◽  
Control Law ◽  
Hardware In The Loop ◽  
Control Performance ◽  
Active System ◽  
Damping Force ◽  
New Model ◽  
Macpherson Strut Suspension ◽  
Macpherson Strut ◽  
Unsprung Mass

Abstract In this paper, a modified skyhook control for the semi-active Macpherson suspension system is investigated. A new model for the Macpherson type suspension, which incorporates the rotational motion of the unsprung mass, is introduced and a feedback control law utilizing the modified skyhook control strategy is derived. Also, two filters to estimate the absolute velocity of the sprung mass and the relative velocity of the rattle space are designed. For testing the control performance, the actual damping force has been included in the hardware-in-the-loop simulations. The control performances of the semi-active system and a passive one have been compared. HILS results are provided.

Analysis of a Sensor Reduction in a Semi-Active Suspension System for a 4-Wheel Vehicle

2010 ◽  
Author(s):  
Cristiano Spelta ◽  
Diego Delvecchio ◽  
Sergio M. Savaresi ◽  
Gabriele Bonaccorso ◽  
Fabio Ghirardo
Keyword(s):  
Phase Shift ◽  
Suspension System ◽  
Control Law ◽  
Frequency Noise ◽  
Vertical Acceleration ◽  
Closed Loop System ◽  
Car Suspension ◽  
Four Corners ◽  
Single Sensor ◽  
Sensor Reduction

This paper is devoted to the application of a comfort-oriented suspension semi-active control system to a four wheel vehicle with a minimal sensor layout. To this aim an algorithm recently developed (the Mix-1-Sensor) has been adopted. The Mix-1-Sensor has been shown to be a quasi-optimal control law for a quarter car suspension system equipped with a single sensor. Therefore a sensor layout of four accelerometers is necessary. This paper shows that it is possible to estimate the vertical acceleration of the four corners by means of only three accelerometers. The estimation suffers from high frequency noise that can be managed by filtering. However this induces a phase-shift of the estimated signal. The closed loop system shows that no phase-shift is preferred since the noise is effectively compensated without a dramatic loss of performances.

Research on Optimal Control and Simulation for Active Suspension Systems

2013 ◽  
Vol 340 ◽  
pp. 631-635
Author(s):  
Yong Fa Qin ◽  
Jie Hua ◽  
Long Wei Geng
Keyword(s):  
Optimal Control ◽  
Ride Comfort ◽  
Mathematic Model ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Vertical Acceleration ◽  
The Road ◽  
Suspension Systems ◽  
Active Suspension Systems

Vehicles with active suspension systems become more ride comfort and maneuverable stability, many types of active suspensions have been applied to passenger vehicles, but one of the shortcomings of an active susupension system is that the additional control power consumption is needed. The core issues of designing an active suspension system are to minimiaze vibration magnitute and control energy comsuption of the active suspension system. A new mathematic model for an active suspension system is established based on vehicle dynamics and modern control theory. An optimal control law is constructed through solving the Riccati equation, and then the transfer function is deduced to describe the relationship between the vetical velosity of the road roughness and the output of suspension system. Three typical parameters of vehicle ride comfort are researched, such as vertical acceleration of vehicle body, dynamic deflection of suspension system and dynamic deformation of tires. A case of a quarter vehicle model is studied by simulation to show that the proposed method of modeling and designing optimal controller are suitable to develop active suspension systems.

scholarly journals Optimal control and parameters design for the fractional-order vehicle suspension system

2018 ◽  
Vol 37 (3) ◽  
pp. 456-467 ◽  
Author(s):  
Hao You ◽  
Yongjun Shen ◽  
Haijun Xing ◽  
Shaopu Yang
Keyword(s):  
Optimal Control ◽  
Fractional Order ◽  
Suspension System ◽  
Least Square ◽  
Vehicle Body ◽  
Vehicle Suspension ◽  
Vertical Acceleration ◽  
Control Force ◽  
Suspension Systems ◽  
Vehicle Suspension System

In this paper the optimal control and parameters design of fractional-order vehicle suspension system are researched, where the system is described by fractional-order differential equation. The linear quadratic optimal state regulator is designed based on optimal control theory, which is applied to get the optimal control force of the active fractional-order suspension system. A stiffness-damping system is added to the passive fractional-order suspension system. Based on the criteria, i.e. the force arising from the accessional stiffness-damping system should be as close as possible to the optimal control force of the active fractional-order suspension system, the parameters of the optimized passive fractional-order suspension system are obtained by least square algorithm. An Oustaloup filter algorithm is adopted to simulate the fractional-order derivatives. Then, the simulation models of the three kinds of fractional-order suspension systems are developed respectively. The simulation results indicate that the active and optimized passive fractional-order suspension systems both reduce the value of vehicle body vertical acceleration and improve the ride comfort compared with the passive fractional-order suspension system, whenever the vehicle is running on a sinusoidal surface or random surface.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

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