One of the most important challenges in re-entry guidance & control is online implementation of an optimal guidance solution. Considering the existence of a pre-set optimal solution, a trajectory tracking guidance is to be designed with the capability to preserve optimal path and boundary constraints. Because of entry point uncertainties and environmental disturbances, the tracker tries to amplify or weaken the optimal guidance signal for properly guiding the vehicle to the reference trajectory. Convergence, tuning and computation burden are among important issues in design of trajectory tracking system. In the present paper, as an innovation the task is followed up by quasi-linear model predictive tracking (QLMPT) which uses MPC as a well-known linear controller in contribution with quasi-linear system of re-entry translational dynamics. It is investigated that above combination, well introduces non-linear tracking behavior. The IGC system is completed through the use of a double-loop attitude controller consisting of Proportional-Integral and Dynamic Inversion to follow guidance commands. The performance of selected IGC system, against disturbances and uncertainties is evaluated by Monte-Carlo analysis. Finally, a PIL test is done to observe hardware implementation performance. Simulation and hardware analysis, results in significant advantages for the proposed method in cases of performance, tuning & processing burden.