Fast Nonlinear Model Predictive Control Parallel Design Using QPSO and Its Applications on Trajectory Tracking of Autonomous Vehicles

Nonlinear Model Predictive Control (NMPC) has begun to apply in the fast dynamical response system. The biggest challenge is how to quickly calculate the optimal solution. In this paper, quantum particle swarm optimization (QPSO) is employed to solve this problem. QPSO has good global searching ability and can avoid falling into local optimal solution. For the constrained system, we propose a generalized Lagrange multiplier method to construct the generalized cost function. By this way, we can transform the optimal solution with constraints into the unconstrained problem. In order to verify the performance of NMPC using QPSO in fast dynamical response system, we apply it to the trajectory tracking of autonomous vehicles. In addition, based on the vehicle kinematics model, the parallel implementation of MPC using QPSO on FPGA is performed to achieve a substantial optimization acceleration in the hardware platform.

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