Optimized Fractional-Order Proportional Integral Derivative Controller for Active Vehicle Suspension System Performance Enhancement

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
A.S. Emam ◽  
H. Metered ◽  
A.M. Abdel Ghany
Keyword(s):  
Fractional Order ◽  
Performance Enhancement ◽  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Vehicle Stability ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Vehicle Suspension System

In this paper, an optimal Fractional Order Proportional Integral Derivative (FOPID) controller is applied in vehicle active suspension system to improve the ride comfort and vehicle stability without consideration of the actuator. The optimal values of the five gains of FOPID controller to minimize the objective function are tuned using a Multi-Objective Genetic Algorithm (MOGA). A half vehicle suspension system is modelled mathematically as 6 degrees-of-freedom mechanical system and then simulated using Matlab/Simulink software. The performance of the active suspension with FOPID controller is compared with passive suspension system under bump road excitation to show the efficiency of the proposed controller. The simulation results show that the active suspension system using the FOPID controller can offer a significant enhancement of ride comfort and vehicle stability.

Investigation and performance comparison of ride comfort on the created human vehicle road integrated model adopting genetic algorithm optimized proportional integral derivative control technique

2020 ◽  
Vol 234 (2) ◽  
pp. 288-305 ◽  
Author(s):  
Arivazhagan Anandan ◽  
Arunachalam Kandavel
Keyword(s):  
Genetic Algorithm ◽  
Ride Comfort ◽  
Active Suspension ◽  
Absolute Error ◽  
Integrated Model ◽  
Suspension System ◽  
Vehicle Stability ◽  
Head Acceleration ◽  
Proportional Integral Derivative ◽  
Proportional Integral

This context exhaustively investigates the ride comfort performance index on the proposed active suspension vehicle system. Ride comfort in terms of occupants (includes driver and passenger) head acceleration, sprung mass vertical and pitching accelerations is considered. For this examination, a 14-degree-of-freedom human vehicle road integrated system model was extensively developed. Then, an active suspension system composed of a hydraulic actuator and proportional-integral-derivative controller is incorporated into the developed vehicle model to enhance the ride comfort. Besides, the designed controller needs to satisfy other vehicle performance indices like vehicle stability and ride safety. Accordingly, the controller parameters were optimally tunned with the help of genetic algorithm technique, on the basis of integral time absolute error criterion. The objective function was created on the basis of minimizing the integral time absolute error of sprung mass displacement, suspension working space and tire deflection responses. The entire response of human vehicle road integrated model, with the proposed active suspension system and passive suspension system on various random road surfaces (A, B, C, D and E with respect to ISO 8608) with five constant speeds (20, 40, 60, 80 and 100 kmph), was compared via surficial presentation. Furthermore, the comfort measures such as root mean square and vibration dose value from ISO 2631-1 were adopted to evaluate the severity between the occupants via head acceleration response. The simulation results showed that the suggested active suspension system significantly improved the ride comfort with guaranteed vehicle stability and ride safety.

Ride Comfort Enhancement of MR-Damped Vehicle Suspension System Based on Fractional Order Fuzzy PI+D Controller

2019 ◽  
Author(s):  
Ahmed Bashir ◽  
Xiaoting Rui ◽  
Adeel Shehzad
Keyword(s):  
Fractional Order ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Ride Quality ◽  
Vehicle Suspension ◽  
Quality Level ◽  
Vertical Acceleration ◽  
Proportional Integral ◽  
Vehicle Suspension System

Abstract In this paper, a fractional order fuzzy proportional-integral plus differential (FOFPI+D) controller is presented for nonlinear vehicle semi-active suspension system (SAS). The control goal is to meliorate the ride quality level by minimizing the root mean square of vehicle body vertical acceleration (RMSVBVA) and maintaining suspension travel. The FOFPI+D controller is realized using non-integer differentiator operator in fuzzy proportional integral (FPI) controller plus the derivative (D) action with additional fractional differentiator. A dynamical model of four degrees–of–freedom vehicle suspension system incorporating magnetorheological dampers (MRD’s) is derived and simulated using Matlab/Simulink software. The performance of the semi-active suspension system using FOFPI+D controller is compared to MR-passive suspension system. The simulation results prove that semi-active suspension system controlled using FOFPI+D outperform and offer better comfort ride under road profiles such as random and bump.

scholarly journals Performance Comparison of Various controllers on Semi-Active Vehicle Suspension System

2021 ◽  
Vol 40 ◽  
pp. 01001
Author(s):  
Sarvesh Walavalkar ◽  
Viraj Tandel ◽  
Rahul Sunil Thakur ◽  
V.V Pramod Kumar ◽  
Supriya Bhuran
Keyword(s):  
Active Suspension ◽  
Suspension System ◽  
Internal Model Control ◽  
Vehicle Body ◽  
Vehicle Suspension ◽  
Inference System ◽  
Proportional Integral ◽  
Suspension Systems ◽  
Model Control ◽  
Vehicle Suspension System

The value of a self-tuning adaptive semi-active control scheme for automotive suspension systems is discussed in this paper. The current vehicle suspension system uses fixed-coeffcient springs and dampers. The ability of vehicle suspension systems to provide good road handling and improve passenger comfort is usually valued. Passive suspension allows you to choose between these two options. Semi-Active suspension(SAS), on the other hand, can provide both road handling and comfort by manipulating the suspension force actuators directly. The semi-active suspension system for a quarter car model is compared to passive and various controllers such as Proportional-Integral, Proportional-Integral-Derivative, Internal model control (IMC)-PID, IMC-PID with filter, FUZZY, and Adaptive-network-based fuzzy inference system(ANFIS) in this analysis. This research could be relevant in the future for designing better car suspension adjustments to eliminate vertical jerks and rolling motion experienced by the vehicle body on bumps and humps.

Multi-objective genetic algorithm fractional-order PID controller for semi-active magnetorheologically damped seat suspension

2015 ◽  
Vol 23 (8) ◽  
pp. 1248-1266 ◽  
Author(s):  
S Gad ◽  
H Metered ◽  
A Bassuiny ◽  
AM Abdel Ghany
Keyword(s):  
Genetic Algorithm ◽  
Fractional Order ◽  
Ride Comfort ◽  
Mr Damper ◽  
Suspension System ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Multi Objective ◽  
Seat Suspension ◽  
Multi Objective Genetic Algorithm

Recently, fractional-order proportional–integral–derivative (FOPID) controllers are demonstrated as a general form of the classical proportional–integral–derivative (PID) using fractional calculus. In FOPID controller, the orders of the derivative and integral portions are not integers which offer more flexibility in succeeding control objectives. This paper proposes a multi-objective genetic algorithm (MOGA) to optimize the FOPID controller gains to enhance the ride comfort of heavy vehicles. The usage of magnetorheological (MR) damper in seat suspension system provides considerable benefits in this area. The proposed semi-active control algorithm consists of a system controller that determines the desired damping force using a FOPID controller tuned using a MOGA, and a continuous state damper controller that calculates the input voltage to the damper coil. A mathematical model of a six degrees–of–freedom seat suspension system incorporating human body model using an MR damper is derived and simulated using Matlab/Simulink software. The proposed semi–active MR seat suspension is compared to the classical PID, optimum PID tuned using genetic algorithm (GA) and passive seat suspension systems for predetermined chassis displacement. System performance criteria are examined in both time and frequency domains, in order to verify the success of the proposed FOPID algorithm. The simulation results prove that the proposed FOPID controller of MR seat suspension offers a superior performance of the ride comfort over the integer controllers.

scholarly journals Feedback control for two-degree-of-freedom vibration system with fractional-order derivative damping

2017 ◽  
Vol 37 (3) ◽  
pp. 554-564
Author(s):  
Canchang Liu ◽  
Chicheng Ma ◽  
Jilei Zhou ◽  
Lu Liu ◽  
Shuchang Yue ◽  
...  
Keyword(s):  
Feedback Control ◽  
Nonlinear Vibration ◽  
Fractional Order ◽  
Suspension System ◽  
Degree Of Freedom ◽  
Vehicle Suspension ◽  
Vibration System ◽  
Fractional Order Derivative ◽  
Vehicle Suspension System ◽  
Two Degree Of Freedom

A two-degree-of-freedom nonlinear vibration system of a quarter vehicle suspension system is studied by using the feedback control method considered the fractional-order derivative damping. The nonlinear dynamic model of two-degree-of-freedom vehicle suspension system is built and linear velocity and displacement controllers are used to control the nonlinear vibration of the vehicle suspension system. A case of the 1:1 internal resonance is considered. The amplitude–frequency response is obtained with the multiscale method. The asymptotic stability conditions of the nonlinear system can be gotten by using the Routh–Hurwitz criterion and the ranges of control parameters are gained in the condition of stable solutions to the system. The simulation results show that the feedback control can effectively reduce the amplitude of primary resonance, weaken or even eliminate the nonlinear vibration characteristics of the suspension system. Fractional orders have an impact on control performance, which should be considered in the control problem. The study will provide a theoretical basis and reference for the optimal design of the vehicle suspension system.

Active Vehicle Suspension Control Using Full State-Feedback Controller

2015 ◽  
Vol 1115 ◽  
pp. 440-445 ◽  
Author(s):  
Musa Mohammed Bello ◽  
Amir Akramin Shafie ◽  
Raisuddin Khan
Keyword(s):  
State Feedback ◽  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Feedback Controller ◽  
Vehicle Suspension ◽  
Passive Suspension ◽  
Full State ◽  
Full State Feedback ◽  
Passive Suspension System

The main purpose of vehicle suspension system is to isolate the vehicle main body from any road geometrical irregularity in order to improve the passengers ride comfort and to maintain good handling stability. The present work aim at designing a control system for an active suspension system to be applied in today’s automotive industries. The design implementation involves construction of a state space model for quarter car with two degree of freedom and a development of full state-feedback controller. The performance of the active suspension system was assessed by comparing it response with that of the passive suspension system. Simulation using Matlab/Simulink environment shows that, even at resonant frequency the active suspension system produces a good dynamic response and a better ride comfort when compared to the passive suspension system.

scholarly journals Enhancing vehicle suspension system control performance based on the improved extension control

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401877386 ◽  
Author(s):  
Hongbo Wang
Keyword(s):  
Fuzzy Control ◽  
System Performance ◽  
Ride Comfort ◽  
Control Method ◽  
Domain Boundary ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Classical Domain ◽  
Vehicle Suspension System ◽  
Takagi Sugeno

Vehicle suspension system is the key part in vehicle chassis, which has influence on the vehicle ride comfort, handling stability, and security. The extension control, which is not constrained by common control method, could further improve the suspension system performance. The 7 degree-of-freedom suspension model is built. The extension controller is designed according to the function differences. In different extension set domains according to the correlation function, the corresponding control strategy is designed to ensure the suspension system obtains optimal performance in the classical domain and expands the controllable range outside the classical domain as large as possible. By adopting game theory, the domain is optimally divided, and the domain boundary control jump is smoothed by introducing Takagi–Sugeno–Kang fuzzy control into the extension control. Through the simulation and results comparison, it is demonstrated that the extension control could further improve the vehicle ride comfort than the optimal control and the extension control ability can be further promoted through domain game and Takagi–Sugeno–Kang fuzzy control. The analysis of the influence of the extension controller parameter varieties on suspension system performance shows that the error-weighted coefficient and control coefficient have significant effect to the suspension system performance.

scholarly journals Design of LQG Controller for Active Suspension without Considering Road Input Signals

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Hui Pang ◽  
Ying Chen ◽  
JiaNan Chen ◽  
Xue Liu
Keyword(s):  
Angular Acceleration ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Vehicle Suspension ◽  
Linear Quadratic ◽  
The Road ◽  
Input Signals ◽  
Vehicle Suspension System ◽  
Weight Coefficients

As the road conditions are completely unknown in the design of a suspension controller, an improved linear quadratic and Gaussian distributed (LQG) controller is proposed for active suspension system without considering road input signals. The main purpose is to optimize the vehicle body acceleration, pitching angular acceleration, displacement of suspension system, and tire dynamic deflection comprehensively. Meanwhile, it will extend the applicability of the LQG controller. Firstly, the half-vehicle and road input mathematical models of an active suspension system are established, with the weight coefficients of each evaluating indicator optimized by using genetic algorithm (GA). Then, a simulation model is built in Matlab/Simulink environment. Finally, a comparison of simulation is conducted to illustrate that the proposed LQG controller can obtain the better comprehensive performance of vehicle suspension system and improve riding comfort and handling safety compared to the conventional one.

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