How to Implement a Digital Twin System Using Lungkuta and Particle Swarm Optimization Algorithms?

I've been trying to replicate a DC-DC converter based on digital twins using Simulink in Matlab, but I've encountered some problems when I was doing the model building, I've used Runge–Kutta to simulate the actual circuit of the converter, and Particle Swarm Optimization Algorithm to get the optimal data needed in the circuit, but now I don't know how to properly link the Particle Swarm Optimization Algorithm with the Runge–Kutta, I'm looking for help, or if anyone who have built a simulation circuit with this model, please share it with me!

Answer 1

[Shishir Reddy]

Hey

As per my understanding, you would like to integrate the Particle Swarm Optimization (PSO) algorithm with the Runge-Kutta method in Simulink for simulating a DC-DC converter. Since I don’t have access to the exact model, here’s a high-level overview and some steps that you can follow –

1. Defining Objective function – MATLAB function must be created that defines the objective function. This function has to run the Simulink model and return a cost based on the simulation results. Computing this cost is explained in further steps.

2. Solver Type – To ensure the Simulink model is set up to use the Runge-Kutta Solver, select the ‘ode45’ solver if you need a more accurate solution and ‘ode4’ solver if you are performing real time simulations.

3. Setup and Run PSO – MATLAB’s built-in ‘particleswarm’ function can be used to perform the optimization. For more information regarding the ‘particleswarm’ function, kindly refer the following documentation. https://www.mathworks.com/help/gads/particleswarm.html

%Defining PSO
options = optimoptions('particleswarm', 'SwarmSize', 30, 'MaxIterations', 100, 'Display', 'iter');

nVars = 2; % Number of parameters to optimize
lb = [0.1, 0.1]; % Lower bounds
ub = [10, 10]; % Upper bounds

%Run PSO
out= particleswarm(@converterObjectiveFunction, nVars, lb, ub, options);

4. Integrating PSO with Simulink – To integrate Particle Swarm Optimization algorithm with Simulink, Each iteration of the PSO should update the model parameters, execute the simulation, and evaluate the resulting performance to guide the optimization process.

This approach allows you to leverage PSO for optimizing parameters in your Simulink model. Adjust the cost function and parameters as needed to fit your specific DC-DC converter model and optimization goals.

I hope this helps.