As more and more autonomous vehicles enter our road, new mechanisms must be considered to ensure the safe coordination between the autonomous vehicles. Al- though many algorithms have been proposed to coordinate autonomous vehicles, few of them have considered the robustness of the solution against disturbances. Therefore, in this master’s thesis, a vehicle coordination algorithm that uses vehicle to vehicle (V2V) communication is design in order to achieve collision free trajectories, while rejecting disturbances. Specifically, a robust tube-based model predictive control (MPC) scheme is proposed in order to control the autonomous vehicle. This controller uses series of zonotopic reachable sets (also known as tube) to compute a set of state and input constraints, which ensure the robust feasibility of the problem. To reduce the computational burden of the MPC opti- mization problem, the vehicle model is reformulated into a pseudo-linear model by transforming its non-linear equations into the linear parameter varying (LPV) form. The disturbance rejection is performed by a H∞-optimal corrective con- troller. Finally, the collision avoidance is achieved by a V2V coordination algo- rithm, in which the lateral bounds of a collision free path are computed. To validate the proposed control scheme, a series of simulations have been performed to test the disturbance rejection of the corrective controller, as well as the vehicle coordination capabilities. The results from these tests show that the proposed controller is effective in coordinating a multiple overtaking maneuver, while rejecting the disturbances.


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