pid autotuning with fuzzy
2016-08-23
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applications [17,18,19,20,21]. New approaches where intelligence is not given to the system from outside, but is acquired by the system through learning, have proven much more successful [11,22]. A major difficulty associated with development of learning control systems is the fact that we do not know a-priori the desired control action, i.e. the output of the controller. However, we do have measures for evaluating the performance of the overall system. Various methods for overcoming this problem have been used [22]. Identifying the plant or better yet, identifying the inverse plant or pseudo inverse plant, has been tried in several works. Neurofuzzy control, genetically optimized fuzzy control, back-propagation through plant and reinforcement learning have been successfully used in recent years [22,23,24,25]. A more cognitively based version of reinforcement learning has also been developed in which a critic constantly assesses the consequences of actuati
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