Passive Suspension Optimization Using Teaching Learning Based Optimization and Genetic Algorithm Considering Variable Speed Over a Bump

2014 ◽  
Author(s):  
Bhargav Gadhvi ◽  
Vimal Savsani
Keyword(s):  
Genetic Algorithm ◽  
Ride Comfort ◽  
Optimization Techniques ◽  
Passive Suspension ◽  
The Road ◽  
Quarter Car Model ◽  
Teaching Learning Based Optimization ◽  
Quarter Car ◽  
Vehicle Suspension System ◽  
Teaching Learning

The main objectives of a vehicle suspension system are to isolate the road excitations to reach the sprung mass of the vehicle and proper road holding. This paper proposes a solution to optimize a quarter car linear passive suspension parameters while passing over a bump with variable speeds to improve the ride comfort and road holding. The Teaching-learning based optimization algorithm (TLBO) is used to solve the problem and results are compared to those obtained by Genetic algorithm (GA) technique. The quarter car model presented is simulated in time domain subjected to a Cosine speed bump considering the variable speeds of the vehicle over it. Results show sprung mass acceleration, and tire displacement are reduced by 26.03%, and 23.7% respectively by using TLBO and 22.3%, and 18.52% respectively by using GA, conforming the capabilities of the optimization techniques.

H∞ Control of Active Suspension System for a Quarter Car Model

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
N.M. Ghazaly ◽  
A.S Ahmed ◽  
A.S Ali ◽  
G.T Abd El- Jaber
Keyword(s):  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Passive Suspension ◽  
Quarter Car Model ◽  
Suspension Systems ◽  
Car Model ◽  
Active Suspension Systems ◽  
Passive Suspension System ◽  
Quarter Car

In recent years, the use of active control mechanisms in active suspension systems has attracted considerable attention. The main objective of this research is to develop a mathematical model of an active suspension system that is subjected to excitation from different road profiles and control it using H∞ technique for a quarter car model to improve the ride comfort and road handling. Comparison between passive and active suspension systems is performed using step, sinusoidal and random road profiles. The performance of the H∞ controller is compared with the passive suspension system. It is found that the car body acceleration, suspension deflection and tyre deflection using active suspension system with H∞ technique is better than the passive suspension system.

scholarly journals Optimal Control Design of Active Suspension System Based on Quarter Car Model

2019 ◽  
Vol 11 (2) ◽  
pp. 55
Author(s):  
Nur Uddin
Keyword(s):  
Optimal Control ◽  
Ride Comfort ◽  
Control Design ◽  
Active Suspension ◽  
Suspension System ◽  
Passive Suspension ◽  
Quarter Car Model ◽  
Car Model ◽  
Passive Suspension System ◽  
Quarter Car

The optimal control design of the ground-vehicle active suspension system is presented. The active suspension system is to improve the vehicle ride comfort by isolating vibrations induced by the road profile and vehicle velocity. The vehicle suspension system is approached by a quarter car model. Dynamic equations of the system are derived by applying Newton’s second law. The control law of the active suspension system is designed using linear quadratic regulator (LQR) method. Performance evaluation is done by benchmarking the active suspension system to a passive suspension system. Both suspension systems are simulated in computer. The simulation results show that the active suspension system significantly improves the vehicle ride comfort of the passive suspension system by reducing 50.37% RMS of vertical displacement, 45.29% RMS of vertical velocity, and 1.77% RMS of vertical acceleration.

scholarly journals Performance Evaluation of Genetic Algorithm & Fuzzy Logic for Portfolio Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 1996-2002
Keyword(s):  
Genetic Algorithm ◽  
Cluster Analysis ◽  
Portfolio Optimization ◽  
Analytical Hierarchy Process ◽  
Hybrid Approach ◽  
Optimization Techniques ◽  
Multiobjective Linear Programming ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Hierarchy Process

Teaching-learning based optimization (TLBO), biogeography-based optimization (BBO) and fuzzy multiobjective linear programming (FMOLP) are compared in this paper for portfolio optimization. A hybrid approach has been adopted for this comparative study which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP) and optimization techniques. Return, risk, liquidity, coefficient of variation (CV) and AHP weighted scores are used as the objective function for optimization.

Evaluation of Human Exposure to Vibrations using Quarter Car Model with Semi-Active Suspension

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
D.V.A. Rama Sastry ◽  
K.V. Ramana ◽  
N. Mohan Rao ◽  
M. Phani Kumar ◽  
V.S.S. Rama Chandra Reddy
Keyword(s):  
Human Body ◽  
Ride Comfort ◽  
Mr Damper ◽  
Active Suspension ◽  
Suspension System ◽  
Lumped Mass ◽  
Passive Suspension ◽  
Quarter Car Model ◽  
Car Model ◽  
Quarter Car

Exposure of human body to vehicular vibrations in transit may lead to the human discomfort. Ride comfort is one of the major issues in design of automobiles. Magneto rheological (MR) dampers are emerging as most feasible solution for various applications in controlling vibrations. An MR damper is a semi-active device, which will offer the advantages of both active and passive suspension. In this study, the MR damper based semi-active suspension system for a car is analysed for ride comfort of 7 degrees of freedom model human body lumped mass, considering head, upper torso, lower torso and pelvis, seated over a seat of a quarter car model and is compared with that of similar system using passive damper. A MR damper is fabricated and is filled with MR fluid made of Carbonyl iron powder and Silicone oil added with additive. Modified Bouc-Wen Model developed by Spencer is used to model the behaviour of MR damper. All the parameters of this model are identified using data acquired from experiments conducted to characterise MR damper. Further, using the Spencer model of MR damper, the human body seated over quarter car is simulated by implementing a semi-active suspension system for analysing the resulting displacement and acceleration of the human body. The ride comfort performance of vehicle model with passive suspension system is compared with corresponding semi-active suspension system. The simulation and analysis are carried out using MATLAB/SIMULINK.

Magnetorheological damper in semi-active vehicle suspension system using SDRE control for a quarter car model

2018 ◽  
Author(s):  
Maria Aline Gonçalves ◽  
Rodrigo Tumolin Rocha ◽  
Frederic Conrad Janzen ◽  
José Manoel Balthazar ◽  
Angelo Marcelo Tusset
Keyword(s):  
Suspension System ◽  
Magnetorheological Damper ◽  
Vehicle Suspension ◽  
Quarter Car Model ◽  
Car Model ◽  
Quarter Car ◽  
Vehicle Suspension System ◽  
Active Vehicle Suspension System

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

PMU Placement Optimization for Fault Observation Using Different Techniques

2021 ◽  
pp. 196-220
Author(s):  
Hamid Bentarzi
Keyword(s):  
Cuckoo Search ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Placement Optimization ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

Airfoil Topology Optimization using Teaching-Learning based Optimization

2015 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Dushhyanth Rajaram ◽  
Himanshu Akhria ◽  
S. N. Omkar
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Spline Curves ◽  
Conceptual Design Phase ◽  
Design Variables ◽  
Heuristic Technique ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper primarily deals with the optimization of airfoil topology using teaching-learning based optimization, a recently proposed heuristic technique, investigating performance in comparison to Genetic Algorithm and Particle Swarm Optimization. Airfoil parametrization and co-ordinate manipulations are accomplished using piecewise b-spline curves using thickness and camber for constraining the design space. The aimed objective of the exercise was easy computation, and incorporation of the scheme into the conceptual design phase of a low-reynolds number UAV for the SAE Aerodesign Competition. The 2D aerodynamic analyses and optimization routine are accomplished using the Xfoil code and MATLAB respectively. The effects of changing the number of design variables is presented. Also, the investigation shows better performance in the case of Teaching-Learning based optimization and Particle swarm optimization in comparison to Genetic Algorithm.

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