Hi Muhammad,
I understand that you are trying to put constraints on values for voltages and power in the actor neural network. I can provide you an example approach and you can modify it according to your model environment and problem requirement.
Since the voltage and power constraints are different, it's better to separate them into two different layers in your actor network. This way, you can apply different constraints to each output.
Now, to enforce the voltage constraints between 0.95 and 1.05, you can add a custom activation layer after the voltage output layer. This activation layer should clamp the output values within the desired range. Here's an example of how you can add the voltage constraints:
voltageOutputLayer = fullyConnectedLayer(2, ...
'BiasLearnRateFactor', 0, 'Bias', Biass(1:2));
voltageActivationLayer = customClampLayer(0.95, 1.05, 'VoltageClamp');
powerOutputLayer = fullyConnectedLayer(4, ...
'BiasLearnRateFactor', 0, 'Bias', Biass(3:6));
actorNet = [
featureInputLayer(obsInfo.Dimension(1))
voltageOutputLayer
voltageActivationLayer
powerOutputLayer
];
In this example, customClampLayer is a custom layer that clamps the values between a specified range. You can implement it as follows:
classdef customClampLayer < nnet.layer.Layer
properties
LowerBound
UpperBound
end
methods
function layer = customClampLayer(lowerBound, upperBound, name)
layer.LowerBound = lowerBound;
layer.UpperBound = upperBound;
layer.Name = name;
end
function Z = predict(layer, X)
Z = max(layer.LowerBound, min(layer.UpperBound, X));
end
end
end
Similarly, you can enforce the power constraints individually for each power output, you can add a custom activation layer after each power output layer. This activation layer should ensure that the power values are positive and do not exceed the maximum power limit.
The given custom activation layers can achieve the required behaviour and constraint the power and voltage values in the given actor neural network. The above approach and code snippet is a starting point, and you can modify it to fit your requirement.
For more information regarding the custom deep learning layer, you can refer to the following documentation https://www.mathworks.com/help/deeplearning/ug/define-custom-deep-learning-intermediate-layers.html.
Hope it helps!
