Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Neural network for robotic control
Neural network for robotic control
Mathematical formulation of a type of hierarchical neurofuzzy system
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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This paper deals with steering and velocity control of outdoor autonomous vehicles. A fuzzy control system with two levels is used to track a path previously recorded or computed by means of a path planning program. In the first level a fuzzy logic system selects a goal point in the path to track taking into account the relative position and orientation of the vehicle, the curvature of the path ahead, and the vehicle's velocity. The second level generates the steering and velocity commands using the goal point provided by the first level and the current values of the vehicle's variables obtained by the sensors. The controller is computed by fuzzy identification using input and output data collected when a human is driving the vehicle. The determination of the number of fuzzy rules and the constant step-size is optimized to improve the convergence. The paper presents the application of the proposed method to the Romeo 3R outdoor mobile robot.