| Summary: | 博士 === 國立交通大學 === 電機與控制工程系 === 87 === To tackle the nonlinearity present in the dynamics of robot manipulators, the learning controllers have been applied for robot motion governing using different kinds of control structures. However, most of them need to repeat the learning process each time a new
trajectory is encountered. Otherwise, a neural network will consist of a huge number of neurons or a fuzzy system will require too many rules because the learning space needed to handle arbitrary trajectories is too large. Inspired by the concept of human motor program, we consider that to generalize the learned motions effectively for governing those unpracticed motions is the key role to reduce the size of the learning space. In this dissertation, a novel robot learning control scheme is proposed to enlarge learning space coverage based on learning space analysis. A new learning control structure in the proposed scheme consists mainly of a fuzzy system and a cerebellar model articulation controller (CMAC)-type neural network. The fuzzy system is used for governing a number of sampled motions in a group of motions which are not yet classified. The CMAC-type neural network is then used to generalize the parameters of the fuzzy system, which are appropriate for the governing of the sampled motions, to deal with the whole group of motions. We also evaluate the similarity between robot motions so as to classify those motions governed by the fuzzy system, and make the subsequent generalization executed by the CMAC-type neural network more effective. Therefore, the learning effort is dramatically reduced in dealing with a wide range of robot motions, while the learning process is performed only once.
|
|---|
