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Multi-Objective Control of an In-Joint Semi-Active Suspension System with Energy Harvesting Capability Using Neural Network

Soleymanzadeh Fard, Sajad | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56096 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Sayyaadi, Hassan
  7. Abstract:
  8. The basic type of suspension systems, which is known as passive suspension systems, creates a balance and compromise between the two goals of ride comfort and road holding. In order to improve the performance of the suspension system, the use of active actuators has been considered. However these actuators require energy and control strategies. To supply the required energy, recovering the energy of vehicle vibrations can be a solution. Therefore, ride comfort, keeping the wheels in contact with the road and the amount of recoverable energy are three important control objectives for modern active and semi-active suspension systems. The control of these systems is considered a challenging task in design with different control objectives being present. The use of linear actuators in suspension systems is the mainstream in the use of active actuators in the suspension system, and many researches have been conducted on them. The use of these actuators improves the performance of the suspension system. However, the use of these actuators increases the unsprung mass, requires a change in the suspension geometry, and has no economic justification for medium-class cars. In order to solve the practical and economic problems of linear actuators, in this research, the idea of using rotary actuators instead of linear actuators in active and semi-active suspension systems is explored. First, by using neural networks, the McPherson suspension system in which rotary actuators are used is identified, then, taking into account the appropriate control objectives, using intelligent control methods, a controller is designed for the system. Finally, the performance improvement of the system using the actuators compared to the passive system.
  9. Keywords:
  10. Semiactive Suspension System ; Neural Network ; Model Predictive Control ; Energy Harvester Systems ; In-Joint Rotary Actuator ; Macpherson Suspension System

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