Design of a fuzzy controller based on genetic algorithm for a 6-DOF parallel manipulator is studied in this thesis. In the analytical part, the study deals with kinematic and dynamic analysis of the manipulator as well as its control synthesis via Parallel Distributed Compensation (PDC) method. In the experimental part, implementation of the controller on a prototype of this kind has been carried out. This manipulator is basically made up of two platforms. The base platform is fixed to the ground and is linked to the moving platform through six legs. Each leg comprises two links with a universal joint connection in between. The lower link is connected to the base platform through a revolute joint and upper link is connected to the moving platform through a spherical joint.Kinematic constraint equations are extracted in both, algebraic and differential forms. As a result, the forward and inverse kinematics of the robot are solved. The full nonlinear dynamic equations of the manipulator are derived using Lagrange’s method for constrained systems. Using orthogonal complement of the constraint Jacobian matrix and eliminating the Lagrange multipliers, dynamic equations are reduced to a set of six independent differential equations.Based on the kinematic and dynamic analysis of the manipulator a Takagi-Sugeno fuzzy model of the system is presented through a combination of linear systems. The concept of PDC is used to design the fuzzy controller for the system. To linearize the nonlinear system, some points in the workspace of the manipulator are chosen and dynamical model is linearized at these points. Stability of the designed fuzzy control system is guaranteed via Lyapunov approach . The sufficient conditions for the existence of an appropriate controller are presented in terms of Linear Matrix Inequalities (LMIs). These LMIs are used to determine the common positive definite matrix and the feedback gains.This manipulator has many singular points, large degrees of freedom and a challenging dynamics to control. Therefore, choosing the points in the workspace of the manipulator to design the fuzzy controller is very important and optimization of the controller is necessary. Therefore, in this thesis to design the optimal PDC fuzzy controller, genetic algorithm is used and the linearizing points are chosen optimally. Due to model based nature of the controller, practically implementation of the controller faced several problems. However, two sliding mode and PID controllers are implemented in the experimental prototype. Therefore, implementation results of controllers are presented at the end. Keywords: Parallel robot, Motion simulator, Rotary actuator, PDC Fuzzy control, Genetic algorithm
