How can I represent the terminal constraint set with respect to the Stability condition with MPC(Model Predictive Control)?

Some people in here may know an approach for guanteeing the recursive feasibility and stability with Model Predictive Ctonrol(MPC).

So, May I give a question in here about that?

To consider the recursive feasibility and statbility in MPC, there are two methods.

First one is making a Lyapunov Function about the terminal penalty cost function and

another one is making a terminal constraint set of Maximal Positive Invariant Set(MPIS)

Ok. In a short what I want to know is just like this:

There is a terminal constraint set,

if the prediction horizon in a problem is equal to Hp, then the terminal constraint should be:

x(Hp) ∈

where is the termal constraint set, MPIS and it is a kind of Polyhedron and it has two matrices, A and b.

And, the main question is here... How can I use this term at the implenmenting step? The two matrices mentioned above, A and b.

A * (x state at the terminal ) <= b or

A* = b. Which one is the right approach???

please help me

Thanks in advance.

Min