Privacy-Preserving Distributed Optimal Control for Vehicular Platoon With Quantization

In this paper, the distributed optimal control for vehicular platoons with quantized states and control inputs is studied and the preservation of vehicles' privacy is investigated. Firstly, the structure of system composed of cloud servers, eavesdroppers, and the vehicular platoon with unknown nonlinear dynamics and disturbance is established. Secondly, to restrain the uncertainty and nonlinearity, a policy iteration algorithm integrating quantized control inputs is proposed to stabilize vehicles without knowing their dynamics. The optimality and convergence of the iteration are proved rigorously. Then, the algorithm is utilized by the actor-critic framework with quantized states and control inputs. Due to the reason that conventional distributed control approaches for platoon will inevitably leak privacy, by introducing leveled fully homomorphic encryption, the actor-critic framework is modified to work in a privacy-preserving manner, and the privacy of each vehicle is protected from unauthorized entities. Finally, the comparison results demonstrate that with proposed scheme, the average instant cost of each vehicle is improved (at least 56.52% under experiment condition) and the stability, efficiency and security of system with proposed method are verified by simulations.