Non-linear model prediction control method based on quantum particle swarm optimization

Abstract

The invention relates to the field of unmanned vehicle control, and provides a parallel design scheme using quantum particle swarm optimization, to ensure that the control output meets the physical constraints of the vehicle and the comfort requirement for a human body so as to enable the vehicle to preferably adapt to the current road condition. The technical scheme of the parallel design schemeusing quantum particle swarm optimization includes the steps: establishing a kinetic model based on an unmanned vehicle, and performing discretization on the kinetic model; based on the above step, constructing a generalized cost function with a punishment item and an encouragement item by using a generalized Lagrangian multiplier so as to convert the constraint problem into a nonrestraint problem; and performing parallel design of quantum particle swarm optimization, performing optimized solution on the cost function of model prediction control by means of the parallel design to obtain a series of controlled variables, and finally acting the first component of the controlled variables on the vehicle. The parallel design scheme using quantum particle swarm optimization is mainly applied tothe unmanned vehicle control occasion.

Description

technical field [0001] The invention relates to the field of control of unmanned vehicles, in particular to a method for optimizing and solving the cost function of non-linear model predictive control in trajectory tracking of unmanned vehicles by a quantum particle swarm algorithm designed in parallel. Background technique [0002] Model predictive control is an advanced control algorithm, because of its unique predictive ability, it has good control effect in many fields and has been widely used in many production processes. However, model predictive control needs to solve a finite time-domain open-loop optimization problem online at each sampling time according to the current measurement information, and this process will consume a lot of time. In order to improve the rapidity of the algorithm, people introduce the concept of control time domain. When the prediction time domain is greater than the control time domain, the optimization solution will no longer be carried o...

AI Technical Summary

Problems solved by technology

However, in the traditional particle swarm optimization algorithm, the motion state of the particles is described by the position and velocity, and the trajectory of the particles is given as time evolves.

At the same time, the moving speed of the particles is limited, so that the search space of the particles is a limited and gradually decreasing area, so that the entire solution space cannot be covered.

Images