Unmanned vehicle speed control method based on PSO and RBF neutral network

Abstract

The invention discloses an unmanned vehicle speed control method based on PSO (Particle Swarm Optimization) and an RBF neutral network. The method comprises the following specific steps: 1, establishing an unmanned vehicle speed control system configuration; 2, building a vehicle speed tracking closed loop control mathematic dynamic model; 3, building an unmanned vehicle speed control driver model based on a fuzzy RBF neutral network structure; 4, fuzzifying an input variable of the driver model to obtain a fuzzy value, and utilizing the fuzzy value to establish an input and output variable membership function; 5, establishing a driver model fuzzy control rule list on the basis of the step 3, the step 4, the experience of a driver and measurement data; 6, calculating the fitness of each rule in the driver model, and completing fuzzification and normalization calculation; 7, establishing an improved PSO control flow on the basis of PSO; and 8, establishing a vehicle speed control flow on the basis of the improved PSO and a fuzzy RBF neutral network algorithm. According to the vehicle speed control method, the vehicle speed tracking error is low, and the anti-interference capability is high.

Description

technical field [0001] The invention belongs to the technical field of automatic driving device control for automobile tests, and in particular relates to a fuzzy neural network control for unmanned driving of electric vehicles capable of realizing accurate tracking of a given target vehicle speed. Background technique [0002] The mileage and energy consumption of electric vehicles are one of the important indicators to directly evaluate the performance of electric vehicles. Traditionally, two methods are used to test the mileage and energy consumption of electric vehicles. The first is manual driving, which makes the test results largely affected by human factors, thus affecting the mileage evaluation index. The second is to test on a dynamometer. Since the dynamometer is controlled according to a certain torque, constant speed, and constant current, and the driving motor of the car is in variable torque and variable speed when driving normally, this kind of test is diffic...

AI Technical Summary

Problems solved by technology

Traditionally, two methods are used to test the mileage and energy consumption of electric vehicles. The first is manual driving, which makes the test results largely affected by human factors, thus affecting the mileage evaluation index.

The second is to test on a dynamometer. Since the dynamometer is controlled according to a certain torque, constant speed, and constant current, and the driving motor of the car is in variable torque and variable speed when driving normally, this kind of test is difficult. Simulate the real driving conditions of electric vehicles

But the training time of RBF is shorter

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