Plasma with an internal transport barrier (ITB) will be developed in JT-60SA as an attractive operation appropriate for a steady-state fusion reactor. To achieve the ITB plasma while avoiding magnetohydrodynamic instabilities, it is advantageous to simultaneously control the safety factor (q) profile and the normalized beta (). In this study, a control system for simultaneous control of the q profile and is studied in simulations prior to the real experiment in JT-60SA. The bootstrap current dominates the total current in the ITB region, which results in a coupling between the pressure profile and the q profile. Thus, it is crucial to control the q profile and according to the strength of the ITB. A two-stage neural network (NN)-based control system was developed to address this problem. The first stage estimates the transport properties (i.e. ITB strength) of the plasma from measurements. The second stage consists of several NNs for control of the q profile and . According to the ITB strength estimated by the NN in the first stage, the appropriate NN for control is selected from those in the second stage. Each NN in the second stage is trained to control plasmas with different ITB strengths through reinforcement learning employing RAPTOR, an integrated transport code. To validate this system, it is tested in a simulation employing another integrated transport code, TOPICS, to mimic the plasma control in JT-60SA plasmas with various ITB strengths. Stable control of the q profile and is achieved in ITB plasmas simulated by both the RAPTOR and TOPICS codes.