a new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot

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abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuzzy logic systems, we presume that we are almost certain about the fuzzy membership functions which is not true in many cases. thus t2fls as a more realistic approach dealing with practical applications might have a lot to offer. type-ii fuzzy logic takes into account a higher level of uncertainty, in other words, the membership grade for a type-ii fuzzy variable is not any longer a crisp number but rather it is itself a type 1 linguistic term [25, 24, 17, 41, 27, 26]. parallel robots on the other hand, are rather new sort of industrial and scientific tools that are being used in diverse research and industrial academia. the most problematic issues that engineers and designers face when using such robots are the high computational complexity needed for calculation of the inverse dynamics which should be recalculated at each movement step along with the structural uncertainty present in the underlying robot. in this thesis i’ll try to consider the effects of uncertainty in dynamic control of a parallel robot. more specifically, it is intended to incorporate the type-ii fuzzy logic into a model based controller, the so-called computed torque control method, and apply the result to a 3 degrees of freedom parallel manipulator. one of the most well-known dynamic controllers that relies on the dynamic calculation of parameters of the underlying robot (in the feedback) is called the computed torque control method. the ctc converts the non-linear dynamics of a robot into a linear one provided that the dynamics of the system at hand is completely identified. having designed a system with a linear dynamic, it is easy for a control engineer to design a pid (or maybe pd) controller for it so that the final motion of the robot would be to follow a predetermined trajectory precisely. the problem with the aforementioned method is that even if we manage to determine the foregoing parameters accurately we are yet to recalculate each matrix for every time step. this imposes a high amount of computational burden. therefore to overcome this demanding task, we should find a closed form formula for each of the dynamic terms so that not to perform intense computations that eventually leads to calculation of theoe parameters again and again. one way as a remedy is to do approximation using classical fuzzy function approximators. however as there is a high amount of uncertainty available in our crisp data samples, we cannot trust on the outputs so much so it might entails an unstable situation. accordingly, a type-ii fuzzy approximator probably performs better although it might add up more complexity. keywords: robot dynamic control, parallel manipulator, 3psp robot, type-ii fuzzy logic, computed torque control metho

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