Driving simulators have been used in the automotive industry for many years now. They have been vastly employed for conducting tests in a safe, reproducible and controlled immersive virtual environment. The ability of the simulator to recreate the in-vehicle experience for the occupant is established through motion cueing algorithms. Such algorithms have consistently been developed with model predictive control (MPC) acting as the main control technique. Currently available MPC based methods either compute the optimal controller online or derive an explicit control law in an offline setting. These approaches limit the applicability of MPC for real-time applications due to online computational expense and offline memory storage issues.

This thesis report presents a solution to deal with issues of offline and online solving through a combined/hybrid approach. For this, explicit MPC is used to provide an initial guess as warm start for the implicit MPC based motion cueing algorithm. From the simulations, it was observed that the presented hybrid approach was able to reduce online computational load by shifting it offline using the explicit MPC. Further, braking constraints and adaptive washout weights were implemented in the hybrid motion cueing algorithm, to improve the specific force tracking performance and reduce any false cue occurrences. Finally, emulator studies were performed to realize driving simulator performance with the hybrid MPC approach. The thesis concludes by showing the improvements made with the developed algorithm and proposes recommendations for future work.