In your Simulink model, you may use a variety of optimization techniques to determine the greatest value of the output for a given range of inputs. Here are a few potential strategies:
Gradient-based optimization : If your model is differentiable, you can identify the ideal input values that maximise the output by using gradient-based optimization methods like gradient descent, conjugate gradient, or BFGS. The tools built into Simulink or custom MATLAB code can be used to determine the gradient of the output with respect to the input variables to achieve this. Using an optimization function like "fmincon" or "fminunc" to look for the ideal input values that maximise the output is possible once you have the gradient.
Genetic algorithms: If your model is nonlinear or nondifferentiable, you can use genetic algorithms (GA) to look for the ideal input values. An evolutionary algorithm known as a GA simulates natural selection and evolution to discover the optimum answer to a problem. You can either create your own MATLAB code or utilise the built-in "Genetic Algorithm" block in Simulink to implement a GA algorithm.
Simulated annealing : Simulated annealing is an additional optimization algorithm that can be applied. It is a probabilistic metaheuristic that searches a sizable search space in quest of the global optimum. To incorporate it into your Simulink model, you can either utilise the built-in "Simulated Annealing" block or develop original MATLAB code.
