For predictions, the Kalman Filter has it's drawbacks. They are clearly visible when you try to use it in conjunction with other algorithms based on state prediction such as Model Predictive Control. The main problem is that the KF does not have integral action.
One quick fix is to add a disturbance model to the KF, for example an integating disturbance. But then you have one more tuning parameter (the disturbance model). Which along the other KF tuning parameters increase complexity and becomes more difficult to implement and understand.
Personally, I have had more success with other types of filters, which have a similar structure as the KF, but include mechanisms for improved predictions, for example this one:
One quick fix is to add a disturbance model to the KF, for example an integating disturbance. But then you have one more tuning parameter (the disturbance model). Which along the other KF tuning parameters increase complexity and becomes more difficult to implement and understand.
Personally, I have had more success with other types of filters, which have a similar structure as the KF, but include mechanisms for improved predictions, for example this one:
https://folk.ntnu.no/skoge/prost/proceedings/ifac2014/media/...