Tuning fuzzy-PID controller Using genetic algorithm

Hello everyone, I want to ask how can tune fuzzy-PID controller Using genetic algorithm

[Sam Chak]

Hi @Tarek, Are you still in search of examples to learn how to tune a Fuzzy PID controller using Genetic Algorithm?

Answer 1

[Dr.GADDALA JAYA RAJU]

Tuning a fuzzy-PID controller using a genetic algorithm (GA) involves optimizing the parameters of the fuzzy logic system and the PID controller simultaneously to achieve desired control performance. Here's a general outline of the steps involved:

1. Define the Fuzzy-PID Controller Structure:

• Determine the input and output variables for the fuzzy logic system (e.g., error, change in error).
• Define linguistic variables (e.g., negative big, negative medium, zero, positive medium, positive big) and membership functions for each input and output variable.
• Establish rules that govern the behavior of the fuzzy logic system, relating the input variables to the output variable.

1. Encode Parameters for Genetic Algorithm:

• Define a chromosome representation that encodes the parameters of both the fuzzy logic system and the PID controller.
• Parameters may include membership function parameters (e.g., centroids, widths), rule weights, PID gains (proportional, integral, derivative gains), etc.

1. Fitness Function:

• Design a fitness function that evaluates the performance of the controller.
• The fitness function should quantify how well the controller performs its task (e.g., minimizing steady-state error, overshoot, rise time).

1. Genetic Algorithm:

• Initialize a population of chromosomes (sets of parameters).
• Evaluate the fitness of each chromosome using the fitness function.
• Select individuals for reproduction based on their fitness (using methods like tournament selection, roulette wheel selection).
• Apply genetic operators (crossover, mutation) to create offspring.
• Repeat the evaluation, selection, and reproduction steps for multiple generations until convergence.

1. Decoding and Evaluation:

• Decode the parameters of the best chromosome (solution) obtained from the genetic algorithm.
• Configure the fuzzy logic system and PID controller with these parameters.
• Simulate the control system and evaluate its performance using various performance metrics.

1. Iterative Improvement:

• If the performance is not satisfactory, refine the fitness function, chromosome representation, or genetic algorithm parameters and repeat the optimization process.

1. Validation:

• Validate the optimized controller on unseen data or in a real-world environment to ensure its effectiveness and robustness.

Remember that tuning a fuzzy-PID controller using a genetic algorithm can be computationally intensive and may require careful tuning of parameters such as population size, mutation rate, and crossover rate to achieve good results efficiently. Additionally, domain expertise in both fuzzy logic systems and PID control is essential for designing effective fuzzy-PID controllers.

[Daniel Uchenna Achilihu]

Hello everyone, I want to ask how can tune fuzzy-PID controller Using bees algorithm

[Sam Chak]

Hi @Daniel Uchenna Achilihu

The Fuzzy PID controller is a model structure that has several variants. It is essential to be clear about the specific parameters that you intend to tune. A practical PID controller typically has four parameters.

The fuzzy logic component of the Fuzzy PID controller can have numerous parameters if you do not have a clear idea of how you want to design the fuzzy logic system for the specific control system.

The Artificial Bee Colony (ABC) algorithm is another optimization technique, similar to the Genetic Algorithm. If you are familiar with tuning a typical controller using the Genetic Algorithm, then utilizing the ABC algorithm with its default hyperparameter settings should not pose a significant challenge.

Take note that many inexperienced students often encounter difficulties in mathematically tuning PID controllers, and consequently resort to utilizing custom tuning algorithms. However, in some cases, these students end up adjusting a greater number of hyperparameters within the sophisticated algorithms than the actual control parameters themselves. For example, you can read the tuning procedures outlined in @Dr.GADDALA JAYA RAJU's answer.