Driving simulators are widely used for understanding Human-Machine Interaction, driver behavior and driver training. The effectiveness of such simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods result in poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. In this thesis, a nonlinear MPC-based algorithm is presented which incorporates the non-linear kinematics of the Stewart platform within the MPC algorithm to increase the effectiveness and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform near its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights based tuning was also observed in the form of smoother actuator movements and better workspace utilization.