scholarly journals LQR Tuning by Particle Swarm Optimization of Full Car Suspension System

2018 ◽  
Vol 7 (2.13) ◽  
pp. 328 ◽  
Author(s):  
Muhammad Assahubulkahfi ◽  
Yahaya Md. Sam ◽  
Andino Maseleno ◽  
Miftachul Huda
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vertical Movement ◽  
Suspension System ◽  
Swarm Optimization ◽  
Quality Of Control ◽  
Car Suspension ◽  
Movement Performance ◽  
Vehicle Suspension System

This paper attempts to examine the potential value in showing the performance of Particle Swarm Optimization (PSO) in order to produce diagonal components of matrix Q, R. The linear model was used in this system, because it has ability to describe all basic performance that exist in full car vehicle suspension system such as roll, pitch, body sprung and each wheel vertical movement. Performance of suspension system measured by range of acceleration arise in automobile body. Drive handling and comfort is an opposite condition. Balancing condition of both define quality of control strategy of suspension system. The disturbances are applied to all tires in testing scenario of applied control algorithm. The simulation result shown better performance of LQR tuning by PSO than passive and LQR without tuning system.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

scholarly journals Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques

2019 ◽  
Vol 39 (2) ◽  
pp. 393-415
Author(s):  
Olurotimi A Dahunsi ◽  
Muhammed Dangor ◽  
Jimoh O Pedro ◽  
M Montaz Ali
Keyword(s):  
Particle Swarm Optimization ◽  
Random Search ◽  
Particle Swarm ◽  
Evolutionary Optimization ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Controlled Random Search ◽  
Evolutionary Optimization Techniques ◽  
Vehicle Suspension System

Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.

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.

scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Sign in / Sign up

Export Citation Format

Share Document