`AutomationLabsSystems.AbstractModel`

— TypeAbstractModel An abstract type that should be subtyped for model extensions (linear or non linear)

`AutomationLabsSystems.ContinuousLinearModel`

— Type`ContinuousLinearModel`

Model linear implementation with AutomationLabs.

** Fields **

`A`

: state matrix.`B`

: input matrix.

`AutomationLabsSystems.ContinuousNonLinearModel`

— Type`ContinuousNonLinearModel`

Model non linear implementation with AutoamtionLabs.

** Fields **

`f`

: the non linear model.`nbr_state`

: the state number.`nbr_input`

: the input number

`AutomationLabsSystems.DenseNet`

— Type`DenseNet`

An densely connected network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.DiscreteLinearModel`

— Type`DiscreteLinearModel`

Model linear implementation with AutomationLabs.

** Fields **

`A`

: state matrix.`B`

: input matrix.

`AutomationLabsSystems.DiscreteNonLinearModel`

— Type`DiscreteNonLinearModel`

Model non linear implementation with AutoamtionLabs.

** Fields **

`f`

: the non linear model.`nbr_state`

: the state number.`nbr_input`

: the input number

`AutomationLabsSystems.Fnn`

— Type`Fnn`

An feedforward neural network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Gru`

— Type`gru`

A gated recurrent unit recurrent neural network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Icnn`

— Type`Icnn`

An input convex neural network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Linear`

— Type`linear`

An linear (Wv –> Ax + Bu) architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Lstm`

— Type`lstm`

A long short-term memory recurrent neural network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.NeuralODE`

— TypeNeuralODE An neural neural network ODE architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.PolyNet`

— Type`PolyNet`

An poly-inception network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Rbf`

— Type`Rbf`

An radial basis neural network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.ResNet`

— Type`ResNet`

An residual layer network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Rknn1`

— Type`Rknn1`

A runge-kutta neural neural network 1 architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Rknn2`

— Type`Rknn2`

A runge-kutta neural neural network 2 architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Rknn4`

— Type`Rknn4`

A runge-kutta neural neural network 4 architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems.Rnn`

— Type`Rnn`

A recurrent neural network architecture type for dynamical system identification problem [ref].

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from non linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from non linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from non linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from non linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from non linear model.

`AutomationLabsSystems._controller_system_design`

— Method*controller*system_design A function for design the system (model and constraints) with MathematicalSystems from non linear model.

`AutomationLabsSystems.proceed_system`

— Method`proceed_system`

A function for system creation from non-linear discrete or continuous model.

The following variables are mendatories:

`f`

: a non-linear function.`nbr_state`

: the state number.`nbr_input`

: the input number.`variation`

: continuous or discrete variation.

It is possible to define optional variables kws.

`AutomationLabsSystems.proceed_system`

— Method`proceed_system`

A function for system creation from linear discrete or continuous model.

The following variables are mendatories:

`A`

: a state matrix.`B`

: a input matrix.`nbr_state`

: the state number.`nbr_input`

: the input number.`variation`

: continuous or discrete variation.

It is possible to define optional variables kws.

`AutomationLabsSystems.proceed_system_constraints_evaluation`

— Method`proceed_system_constraints_evaluation`

Function that return the constraints of a AutomationLabsSystem.

** Required fields **

`system`

: the mathematital system that as in it the julia linear or non-linear function`f`

.

`AutomationLabsSystems.proceed_system_discretization`

— Method`proceed_system_discretization`

Function that linearises a system from MathematicalSystems at state and input references. The function uses ForwardDiff package and the jacobian function.

** Required fields **

`system`

: the continuous mathematital system that as in it the julia linear or non-linear function`f`

.`sample_time`

: the sample time for discretization.

`AutomationLabsSystems.proceed_system_evaluation`

— Method`proceed_system_evaluation`

Function that return the MathematicalSystems type of a AutomationLabsSystem.

** Required fields **

`system`

: the mathematital system that as in it the julia linear or non-linear function`f`

.

`AutomationLabsSystems.proceed_system_linearization`

— Method`proceed_system_linearization`

Function that linearises a system from MathematicalSystems at state and input references. The function uses ForwardDiff package and the jacobian function.

** Required fields **

`system`

: the mathematital system that as in it the julia non-linear function`f`

.`state`

: references state point.`input`

: references input point.

`AutomationLabsSystems.proceed_system_model_evaluation`

— Method`proceed_system_model_evaluation`

Function that return the types of the model inside the systems from AutomationLabsIdentification.

** Required fields **

`system`

: the mathematical system that as in it the julia non-linear function`f`

from AutomationLabsIdentification.