Unmanned vehicles intelligent control methods research

Unmanned vehicle intelligent control methods are advantaged in this paper. For path control of an unmanned vehicle, tracking method is proposed based on neural network. A neural network is made through experiments. Neural network's input are velocity, friction coefficient, hope radius, output is velocity difference. Then prevision control method is used to steering control. This neural network control method can adapt different velocity, ground surface and turning radius. Control method is simple and reliable. For steering control of wheeled mobile robots with complex mathematical models, a multi-step neural network is proposed. Neural networks learn speed, maximum overshoot, overshoot time and steady steering angle in different cases in a reduced learning capacity. As for turning control of wheeled robots, fuzzy neural network model and GA (genetic algorithm) PID control method can be used. Fuzzy GA PID control algorithm is simple, and efficiency of PID parameters can be judged directly. A GA fuzzy neural network is used for steering control of wheeled mobile robots. At first, a neural network model of mobile robot is established. Then, a fuzzy neural network controller is constructed, and GA method is used to find best control parameters. Combining direction and speed control of wheeled mobile robots, GA fuzzy neural networks are used. At first, a fuzzy neural network controller is built, then, a GA optimum algorithm is used to find best parameters for controller. All methods and algorithms proposed in this paper are simulated and tested. Simulation and experiment results show that it is efficient and reliable.