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PID Controller tuning using Jellyfish Search algorithm in MATLAB

PID Controller tuning using Jellyfish Search algorithm in MATLAB

Greetings, viewers! In today's LMS solution, we'll explore the intriguing world of optimization by delving into the application of the Jellyfish Algorithm to fine-tune the parameters of a Proportional-Integral-Derivative (PID) controller. Using a Simulink model, we aim to optimize the PID controller for a second-order plant, enhancing its performance through the minimization of mean absolute error.


Simulink Model Overview:

The simulation model encompasses a second-order plant controlled by a PID controller. The feedback loop measures the output of the plant, which is then compared with the desired output. The goal is to optimize the PID parameters (KP, KI, and KD) by minimizing the mean absolute error. The Jellyfish Algorithm, inspired by the behavior of jellyfish in the ocean, is employed for this optimization task.

Jellyfish Algorithm and Optimization:

The Jellyfish Algorithm is derived from the natural behavior of jellyfish in the ocean, and its stages include six key steps. The algorithm is designed to mimic the movement of jellyfish in response to ocean currents, providing a unique approach to optimization. The mean absolute error is used as the objective function for optimization.

Algorithm Implementation:

The Simulink model is coupled with the Jellyfish Algorithm to iteratively optimize the PID parameters. The MATLAB code for the Jellyfish Algorithm is provided, including key parameters such as the number of populations and the maximum number of iterations. The algorithm's stages, including problem definition, parameter definition, and cost function calculation, are detailed.

Simulation and Results:

The simulation is run for multiple iterations (100 in this case) to achieve convergence. The optimized values for KP, KI, and KD are obtained after the completion of iterations. The results showcase the effectiveness of the Jellyfish Algorithm in optimizing the PID controller for the given plant.

Conclusion:

In conclusion, the blog post provides a comprehensive overview of the optimization of PID controller parameters using the Jellyfish Algorithm within a Simulink model. The algorithm's unique approach, inspired by the behavior of jellyfish, proves to be a promising optimization tool. The post emphasizes the need for multiple trials to achieve better results and showcases the final PID parameters obtained through the optimization process.

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