scholarly journals Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper

2014 ◽  
Vol 6 ◽  
pp. 816813 ◽  
Author(s):  
Mahmut Paksoy ◽  
Rahmi Guclu ◽  
Saban Cetin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Vibration Reduction ◽  
Mr Damper ◽  
Suspension System ◽  
Fuzzy Logic Controllers ◽  
Vehicle Model ◽  
Full Vehicle ◽  
Self Tuning ◽  
Semiactive Suspension

Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.

scholarly journals Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Abroon Jamal Qazi ◽  
Clarence W. de Silva ◽  
Afzal Khan ◽  
Muhammad Tahir Khan
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Fuzzy Logic Controller ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Active Suspension ◽  
Suspension System ◽  
Swarm Optimization ◽  
Quarter Model ◽  
Semiactive Suspension

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.

scholarly journals The Control of an Active Seat Suspension Using an Optimised Fuzzy Logic Controller, Based on Preview Information from a Full Vehicle Model

Vibration ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 20-40 ◽  
Author(s):  
Abdulaziz Alfadhli ◽  
Jocelyn Darling ◽  
Andrew Hillis
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Ride Comfort ◽  
Vertical Direction ◽  
Front Axle ◽  
Rule Base ◽  
Vertical Acceleration ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
Full Vehicle

The use of suspension preview information obtained from a quarter vehicle model (QvM) to control an active seat has been shown by the authors to be very promising, in terms of improved ride comfort. However, in reality, a road vehicle will be subjected to disturbances from all four wheels, and therefore the concept of preview enhanced control should be applied to a full vehicle model. In this paper, different preview scenarios are examined, in which suspension data is taken from all or limited axles. Accordingly, three control strategies are hypothesized—namely, front-left suspension (FLS), front axle (FA), and four wheel (4W). The former utilises suspension displacement and velocity preview information from the vehicle suspension nearest to the driver’s seat. The FA uses similar preview information, but from both the front-left and front-right suspensions. The 4W controller employs similar preview information from all of the vehicle suspensions. To cope with friction non-linearities, as well as constraints on the active actuator displacement and force capabilities, three optimal fuzzy logic controllers (FLCs) are developed. The structure of each FLC, including membership functions, scaling factors, and rule base, was sequentially optimised based on improving the seat effective amplitude transmissibility (SEAT) factor in the vertical direction, using the particle swarming optimisation (PSO) algorithm. These strategies were evaluated in simulation according to ISO 2631-1, using different road disturbances at a range of vehicle forward speeds. The results show that the proposed controllers are very effective in attenuating the vertical acceleration at the driver’s seat, when compared with a passive system. The controller that utilised suspension preview information from all four corners of the car provided the best seat isolation performance, independent of vehicle speed. Finally, to reduce the implementation cost of the “four suspension” controller, a practical alternative is developed that requires less measured preview information.

scholarly journals Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

2020 ◽  
Vol 20 (3) ◽  
pp. 1653
Author(s):  
M.Z. Ismail ◽  
M.H.N. Talib ◽  
Z. Ibrahim ◽  
J. Mat Lazi ◽  
Z. Rasin
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Adaptive Controller ◽  
Tuning Parameter ◽  
Model Reference ◽  
Wide Range ◽  
Speed Performance ◽  
Self Tuning ◽  
Model Reference Adaptive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the   speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>

Multi-objective optimization of the suspension system parameters of a full vehicle model

2018 ◽  
Vol 20 (1) ◽  
pp. 151-177 ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas
Keyword(s):  
Suspension System ◽  
Multi Objective Optimization ◽  
Vehicle Model ◽  
System Parameters ◽  
Multi Objective ◽  
Full Vehicle

Self Tuning Based Adaptive Fuzzy Logic Controller in Lab view for Sterilizing Equipments

2014 ◽  
Vol 3 (2) ◽  
pp. 84
Author(s):  
P. J. Ragu
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Lab View ◽  
Adaptive Fuzzy Logic ◽  
Self Tuning ◽  
Input Variables ◽  
Temperature Errors

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning  Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

Control of a Projectile Smart Fin Using an Inverse Dynamics-Based Fuzzy Logic Controller

2007 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Paul Weinacht ◽  
Venkat Mudupu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Attractive Alternative ◽  
Fuzzy Logic Controllers ◽  
Novel Approach ◽  
External Moment ◽  
Element Approach ◽  
Simulation Results

The objective of this paper is to explore a method for the design of fuzzy logic controller for a smart fin used to control the pitch and yaw attitudes of a subsonic projectile during flight. Piezoelectric actuators are an attractive alternative to hydraulic actuators commonly used in this application due to their simplicity. The proposed cantilever-shaped actuator can be fully enclosed within the hollow fin with one end fixed to the rotation axle of the fin while the other end is pinned at the trailing edge of the fin. The paper includes a dynamic model of the system based on the finite element approach. The model includes external moment due to aerodynamic effects. This paper presents a novel approach for automatically creating fuzzy logic controllers for the fin. This approach uses the inverse dynamics of the smart fin system to determine the ranges of the variables of the controllers. Simulation results show that the proposed controller can successfully drive smart fin under various operating conditions.

Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

2016 ◽  
Vol 23 (3) ◽  
pp. 501-514 ◽  
Author(s):  
Mat Hussin Ab Talib ◽  
Intan Zaurah Mat Darus
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Mr Damper ◽  
Suspension System ◽  
System Response ◽  
Swarm Optimization ◽  
Magneto Rheological

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force–displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL–PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.

Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller

2014 ◽  
Vol 333 (21) ◽  
pp. 5244-5268 ◽  
Author(s):  
Nemanja D. Zorić ◽  
Aleksandar M. Simonović ◽  
Zoran S. Mitrović ◽  
Slobodan N. Stupar ◽  
Aleksandar M. Obradović ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Free Vibration ◽  
Vibration Control ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Composite Beams ◽  
Smart Composite ◽  
Self Tuning
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