In short, you cannot. SSEST is a black-box identification function (unless you pass in a full initialized @idss model as input), which means that the state vector used by the identified model are arbitrary.
Note:
- If you need to check if you really need 8 states, you can reduce an identified model using BALRED and then refine
sys = ssest(data, 8);
sys2 = balred(sys, 5);
sys3 = ssest(data, sys2)
- You can linearize your original model at the given operating point. Let's say that that linearized model is called LinSys. LinSys should be state-consistent with the original model (states of LinSys are deviations from the equilibrium values of those of the nonlinear system), as long as block reduction did not remove any states. Then, you can pass LinSys as initial model to SSEST, along with the deviation experiment data. This was you can update the matrices of LinSys to better fit the data.
sys = ssest(data, LinSys)
