Intelligent fleet longitudinal following control method based on fuzzy model predictive control

A technology of predictive control and fuzzy model, applied in the field of vehicle formation control, can solve the problems of unable to adapt to the driving environment, unable to meet the longitudinal following and stability of the unmanned fleet.

Active Publication Date: 2020-12-29
JIANGSU UNIV
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Problems solved by technology

However, in the solution of the objective function, the weight coefficients are all set to fixed values, which makes it unable to adapt to the changing driving environment and cannot meet the goals of longitudinal followability and stability of the unmanned fleet

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  • Intelligent fleet longitudinal following control method based on fuzzy model predictive control
  • Intelligent fleet longitudinal following control method based on fuzzy model predictive control
  • Intelligent fleet longitudinal following control method based on fuzzy model predictive control

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Embodiment Construction

[0069] Such as figure 1 As shown, an intelligent platoon longitudinal following control system based on fuzzy model predictive control adopts an upper-lower hierarchical structure. The upper-level controller includes a fuzzy controller and a model predictive controller. , the output error weight coefficient scaling factor Q j as input to the model predictive controller. According to the vehicle information and the variable error weight coefficient Q, the model predictive controller performs online rolling optimization to solve the ideal acceleration. The lower-level controller is responsible for converting the ideal acceleration output by the upper-level controller into throttle opening or brake pressure and transmitting it to the vehicle's actuators to control the vehicle to achieve longitudinal follow-up smoothly and quickly.

[0070] Concrete embodiment of the present invention and implementation steps are as follows:

[0071] The first step is to establish a longitudina...

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Abstract

The invention relates to the field of vehicle formation control, and particularly relates to an intelligent fleet longitudinal following control method based on fuzzy model predictive control, and aims to improve the flexibility of a model predictive control algorithm applied to a fleet longitudinal following control system and ensure that following vehicles in a fleet can quickly and stably realize longitudinal following. The method comprises the following steps of: establishing a fleet dynamics model, establishing an upper control system based on fuzzy model predictive control, and establishing a lower control system based on a vehicle inverse dynamics model. In the design of the upper control system, a fuzzy control strategy is introduced, an error weight coefficient in an objective function is adjusted according to the size of a spacing error and a speed error, the response time of the fleet to enter a stable state is accelerated, and the following stability of the vehicles is improved when the fleet is close to the stable state. The method can ensure that the upper control system can output more accurate and reasonable expected acceleration under the condition of meeting multiple constraints, and the driving stability and riding comfort of the fleet are improved.

Description

technical field [0001] The invention relates to the field of vehicle formation control, in particular to an intelligent fleet longitudinal following control method based on fuzzy model predictive control. Background technique [0002] In recent years, with the rapid development of related technologies such as intelligent networked vehicles, driverless vehicles, and Internet of Vehicles, vehicle formation control is also developing in a more complex and comprehensive direction. From simple PID control to LQR control with excellent performance in single-input and single-output systems, to model predictive control (MPC) suitable for multi-constraint nonlinear systems, more advanced control theory methods are gradually being applied to fleet longitudinal Following control. [0003] The main advantage of the model predictive control method is the online optimization of the system, which implements closed-loop control in a progressive manner, thereby solving the optimization prob...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02G05B13/04
CPCG05D1/0295G05B13/042
Inventor 雷利利张通王梓
Owner JIANGSU UNIV
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