Abstract
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).
Highlights
Traditional control of non-linear dynamical systems has been done by using Classical Linear Control Theory and assuming simple linear mathematical models for the systems
We proposed in this paper the application of a hybrid approach for the problem of control, combining neural and fuzzy technologies called Adaptive neuro fuzzy inference system (ANFIS)
Simulation results To show the contribution of the control by artificial neural network fuzzy inference system (ANFIS) and its improvements compared to the Neural Network method, simulation was approved on an unmanned vehicle
Summary
Traditional control of non-linear dynamical systems has been done by using Classical Linear Control Theory and assuming simple linear mathematical models for the systems. The fourth layer calculates the degrees to which output membership functions are matched by the input data, and the fifth layer does defuzzification and calculates exact values for the output variables. As it is illustrated, at the beginning of the simulation, we inject the fuzzy knowledge base (Takagi Sugeno coefficient) on the network controller, we give result in an entry (the initial positions of the reference route (X, Y), the velocity V, back to donkey D, F red light, vehicle O which shows the state of overshoot) and three outputs (new positions (X, Y), and the velocity V). Rule 3: IF Position is X, Y and Obstacle is Null the Position is X, Y
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