OPTIMIZATION OF A FUZZY CONTROL DESIGN WITH RESPECT TO A PARALLEL MECHANISM WORKSPACE

The Ph.D. thesis is focused on using the fuzzy logic for control of a parallel manipulator based on a Stewart platform. The proposed mechanism makes possible to simulate the physiological movements of the human body and observe degradation processes of the cord implants. Parallel manipulators such as a Stewart platform represent a completely parallel kinematic mechanism that has major differences from typical serial link robots. However, they have some drawbacks of relatively small workspace and difficult forward kinematic problems. Generally, forward kinematic of a parallel manipulators is very complicated and difficult to solve. This thesis presents a simple and efficient approach to design simulation model of forward kinematic based on Takagi-Sugeno type fuzzy inference system. The control system of the parallel manipulator id based on state-space and fuzzy logic controllers. The proposed fuzzy controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design method is based on anfis (adaptive neuro-fuzzy inference system) training routine. It utilizes a combination of the least-squares method and the backpropagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controllers are used for the control of a linear actuator. The capabilities of the designed control system are shown on verification experiment