System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization...

Area of Science:

• Automotive Engineering
• Control Systems
• Biomedical Engineering

Background:

• Active suspension systems are crucial for vehicle stability and ride comfort.
• Traditional PID controllers struggle with nonlinear dynamics and parameter uncertainties in active suspensions.
• Enhancing ride comfort is vital for individuals with spinal cord injuries.

Purpose of the Study:

• To develop an advanced control solution for a quarter-car active suspension system.
• To improve ride comfort for spinal cord injury patients while maintaining vehicle stability.
• To overcome limitations of traditional PID controllers using hybrid optimization.

Main Methods:

• A hybrid optimization framework was employed for tuning Proportional-Integral-Derivative (PID) controllers.
• System parameters were selected using Sequential Quadratic Programming and Pattern Search.
• Metaheuristic algorithms (PSO, GA, SA) and hybrid strategies (ACO-GA, Fmin-SA) were utilized for controller design.
• Integral Square Error (ISE) was the primary performance metric for controller selection.

Main Results:

• The Ant Colony Optimization with Genetic Algorithm (ACO-GA) hybrid strategy yielded superior PID controller performance.
• The ACO-GA-based PID controller demonstrated a faster response compared to other tested approaches.
• The ISE criterion consistently provided better results for controller design.

Conclusions:

• Hybrid optimization frameworks, particularly ACO-GA, offer a robust solution for active suspension control.
• The developed controller significantly enhances ride comfort and vehicle stability.
• This approach holds promise for improving mobility and quality of life for individuals with spinal cord injuries.