Attitude control of a satellite using fuzzy controllers
Expert Systems with Applications: An International Journal
Adaptive fuzzy sliding mode control for flexible satellite
Engineering Applications of Artificial Intelligence
A novel hybrid learning technique applied to a self-learning multi-robot system
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An experimental adaptive fuzzy controller for differential games
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A Reinforcement Learning Adaptive Fuzzy Controller for Differential Games
Journal of Intelligent and Robotic Systems
Reinforcement learning-based tuning algorithm applied to fuzzy identification
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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The attitude control of a satellite is often characterized by a limit cycle, caused by measurement inaccuracies and noise in the sensor output. In order to reduce the limit cycle, a nonlinear fuzzy controller was applied. The controller was tuned by means of reinforcement learning without using any model of the sensors or the satellite. The reinforcement signal is computed as a fuzzy performance measure using a noncompensatory aggregation of two control subgoals. Convergence of the reinforcement learning scheme is improved by computing the temporal difference error over several time steps and adapting the critic and the controller at a lower sampling rate. The results show that an adaptive fuzzy controller can better cope with the sensor noise and nonlinearities than a standard linear controller