Vehicle nonlinear state fusion estimation method for cloud control intelligent chassis

Abstract

The invention discloses a vehicle nonlinear state fusion estimation method for a cloud control intelligent chassis. The method comprises the following steps: establishing a vehicle four-degree-of-freedom motion differential equation containing centroid longitudinal, lateral, yaw and roll motions; establishing a vehicle nonlinear state equation and an observation equation according to the vehicle four-degree-of-freedom motion differential equation; performing approximate linearization processing on the nonlinear state equation and the observation equation of the vehicle to obtain a state equation and an observation equation of vehicle state parameters; and iterating the linearized motion state parameters to a local strong tracking filter to obtain a local unbiased estimation value of the nonlinear state of the vehicle, and performing linear combination optimal fusion on the obtained local unbiased estimation value to obtain a nonlinear state fusion estimation value of the vehicle, thereby realizing closed-loop control of the vehicle chassis subsystem. On the basis of effectively solving the problem of filtering divergence caused by a traditional extended Kalman filtering state estimation method, the problem of large estimation error caused by overlarge error of individual sensors is solved.

Description

technical field [0001] The invention belongs to the field of intelligent driving environment perception, and in particular relates to a vehicle nonlinear state fusion estimation method for cloud-controlled intelligent chassis. Background technique [0002] The current vehicle chassis control system is developing in the direction of functional diversification, system integration, volume miniaturization and communication network, and the control system has become more and more complex. In the vehicle chassis active control system, key state variables such as yaw rate, vertical and lateral speed, and body side slip angle, which characterize the vehicle's own running state, are not only the main control variables in the vehicle chassis integrated control system, but also the real-time identification of the vehicle's driving state and It is an important basis for formulating the coordinated control rules of chassis subsystems. These state variables are usually directly measured ...

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Problems solved by technology

[0004] Aiming at the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a vehicle nonlinear state fusion estimation method for cloud-controlled intelligent chassis. Based on the vehicle nonlinear dynamic model, the present invention integrates the strong tracking filter theory And multi-sensor information fusion technology is introduced into the nonlinear state estimation of vehicles. On the basis of effectively solving the filter divergence problem caused by the traditional extended Kalman filter state estimation method, it further overcomes the problem of large estimation errors caused by excessive individual sensor errors. problems, and improve the incompleteness of vehicle state information measurement, so as to realize real-time fusion estimation of key variables for judging vehicle driving state and even key variables for active control of cloud-controlled intelligent chassis

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