Flood Algorithm-Tuned PID-F Controller with a Modified Objective Function for Robust and Noise-resilient Speed...

Area of Science:

• Automotive Engineering
• Control Systems Theory
• Computational Intelligence

Background:

• Nonlinear dynamics and time-varying behavior of spark ignition (SI) engines pose challenges for precise speed control.
• Existing engine speed control strategies often struggle with robustness and rapid response to disturbances.

Purpose of the Study:

• To develop and validate a novel control strategy for regulating engine speed in nonlinear four-cylinder SI engines.
• To leverage the flood algorithm (FLA) for efficient tuning of a proportional-integral-derivative with filter (PID-F) controller.
• To enhance transient response and steady-state accuracy while ensuring robustness against load variations and noise.

Main Methods:

• Integration of a proportional-integral-derivative controller with a filter (PID-F).
• Tuning of the PID-F controller parameters using the flood algorithm (FLA), employing a modified objective function to minimize overshoot and tracking error.
• Modeling and simulation in MATLAB/Simulink.
• Benchmarking against Simulink PID tuner and other metaheuristic algorithms (whale optimization, sinh-cosh, cuckoo search).

Main Results:

• The FLA-optimized PID-F controller demonstrated superior performance compared to existing methods.
• Achieved stable, robust, and noise-resilient engine speed regulation under varying load and disturbance conditions.
• Validated the efficiency and scalability of the FLA for real-time controller tuning.

Conclusions:

• The proposed FLA-tuned PID-F controller offers a practical and computationally efficient solution for advanced engine control.
• Flood algorithm is an effective tool for optimizing controller parameters in automotive applications.
• The strategy enhances engine performance and fuel efficiency through precise speed control.