Evolution of Neural Networks for Helicopter Control: Why Modularity Matters

The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so

A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues

Controlling a drone: Comparison between a based model method and a fuzzy inference system

International audienceThe work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind

A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

Fuzzy logic system promises an efficient way for obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. Reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, supervised learning method is used to determine the membership functions for the input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for the output variables. For sufficient learning, a new learning method using modified Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty in acquiring large amount of training data with high consistency for the supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, the training data are readily obtained and used to train the neural fuzzy system.published_or_final_versio