If the identified model has complex poles, you can't wish them away by converting to zpk. Note that complex poles come in conjugate pairs, so a real filter can still be created. If you are looking for a modal separation (sum of first and second order transfer functions), see modalfit in Signal Processing Toolbox.
Regarding high order TF identification with TFEST: Note that a high order transfer function is going to be ill-conditioned. If you need to work with such high orders, I would stringly suggest using state-space identification (see SSEST).
5 Comments
Romain Liechti (view profile)
Direct link to this comment
https://www.mathworks.com/matlabcentral/answers/473893-converting-high-order-transfer-functions-model-to-discrete#comment_729430
Romain Liechti (view profile)
Direct link to this comment
https://www.mathworks.com/matlabcentral/answers/473893-converting-high-order-transfer-functions-model-to-discrete#comment_729436
Rajiv Singh (view profile)
Direct link to this comment
https://www.mathworks.com/matlabcentral/answers/473893-converting-high-order-transfer-functions-model-to-discrete#comment_729569
Romain Liechti (view profile)
Direct link to this comment
https://www.mathworks.com/matlabcentral/answers/473893-converting-high-order-transfer-functions-model-to-discrete#comment_729570
Romain Liechti (view profile)
Direct link to this comment
https://www.mathworks.com/matlabcentral/answers/473893-converting-high-order-transfer-functions-model-to-discrete#comment_729701
Sign in to comment.