This paper proposed a multi-objective guaranteed feasible CAV platoon trajectory control method for signalized isolated intersections with priorities. Specifically, we prioritize the intersection throughput and traffic efficiency under the pre-defined signal cycle, based on which we minimize fuel consumption and emissions for the CAV platoons. To handle aforementioned targets, we firstly designed a vehicular sub-platoons splitting algorithm based on Farkas lemma to accommodate maximum amount of vehicles for each signal green time phase. Secondly, the CAV optimal trajectories control algorithm was designed as a centralized cooperative model prediction control (MPC). Moreover, the optimal control problem was formulated as discrete linear quadratic control problems with constraints with receding predictive horizons, which can be efficiently solved by quadratic programming after reformulation. For rigor, the proofs of the recursive feasibility and asymptotic stability of our proposed predictive control model are provided. For evaluation, the performance of the control algorithm was compared against (i) no automation scenario and (ii) non-cooperative distributed CAV control via simulation. It was found that the proposed method can significantly enhance the control efficiency and capacity, and has great potential for real-time implementation. Keywords: isolated signalized intersection CAV platoon control, multi-objective optimal trajectory control, guaranteed feasibility, asymptotical stability, sub-platoons splitting algorithm, model predictive control(MPC)

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