One of the ways you can integrate an ANN model with an Extended or Unscented Kalman Filter (UKF) in Simulink is by careful formulation of the state transition and measurement functions. Refer the steps below:
1. Since your ANN model is a black-box nonlinear system, you need to approximate its behavior using state-space representations:
- State Transition Function: Defines how the system evolves over time. If your ANN predicts the next state based on the current state, you can use:
where ( x_k ) is the state, ( u_k ) is the control input, and ( w_k ) is process noise. - Measurement Function: Defines how the system measurements relate to the state:
where ( z_k ) is the measurement and ( v_k ) is measurement noise.
For EKF, you need Jacobians of these functions, while UKF uses sigma points to approximate nonlinear transformations.
2.Simulink implementation -
- Use MATLAB Function Blocks to define the state transition and measurement functions.
- Implement the EKF or UKF using Simulink’s trackingEKF or trackingUKF blocks.
- Connect the ANN model output to the filter’s prediction step.
3. Streaming Live Data
- Use Simulink’s From Workspace or UDP blocks to stream real-time data.
- Compare ANN predictions with live measurements and update the filter’s estimate.
Refer to the following github repository and coumentation link that implements SOC estimation using EKF and UKF in Simulink, and provides detailed documentation on EKF and their implementation:
