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# PID Controller Tuning using Jellyfish Search Algorithm

PID Controller Tuning using Jellyfish Search Algorithm

Introduction:

We will delve into the optimization of Proportional-Integral-Derivative (PID) controller parameters using the Jellyfish Algorithm. The simulation model developed in Simulink aims to optimize the PID controller's parameters (Kp, Ki, and Kd) for controlling a second-order plant. By minimizing the mean absolute error, the Jellyfish Algorithm efficiently tunes the PID controller to enhance the system's performance.

Model Overview:

The Simulink model comprises a second-order plant controlled by a PID controller. The plant's output serves as feedback, which is compared with the desired setpoint to compute the error. The objective is to minimize the mean absolute error by optimizing the PID controller's parameters using the Jellyfish Algorithm.

Jellyfish Algorithm:

The Jellyfish Algorithm is a metaheuristic optimization technique inspired by the behavior of jellyfish in the ocean. It involves several stages, including passive and active motion, to explore the solution space effectively. By iteratively updating the controller parameters based on the objective function (mean absolute error), the algorithm converges towards the optimal solution.

Implementation Steps:

1. Problem Definition: Define the optimization problem, including the objective function and constraints.

2. Jellyfish Algorithm Parameters: Specify the algorithm parameters such as population size and maximum number of iterations.

3. Cost Function Calculation: Calculate the cost function (mean absolute error) based on the current PID controller parameters.

4. Iterative Optimization: Iterate through the algorithm stages, including passive and active motion, to update the controller parameters.

5. Parameter Update: Update the PID controller parameters based on the algorithm's search process.

6. Simulation and Results: Simulate the model with the optimized PID controller parameters and observe the system's response.

Simulation Results:

Upon completion of the Jellyfish Algorithm optimization process, the simulation results demonstrate the effectiveness of the tuned PID controller. The system exhibits improved control performance, with reduced error and faster response times, thereby enhancing overall system stability and efficiency.

Conclusion:

The utilization of the Jellyfish Algorithm for optimizing PID controller parameters offers a robust and efficient approach to enhance control system performance. By iteratively adjusting the controller parameters based on the mean absolute error, the algorithm effectively tunes the controller for optimal performance under varying operating conditions.

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