POSITION CONTROL OF 5 DOF ROBOT MANIPULATOR USING GA BASED FUZZY LOGIC CONTROLLER

Abstract

In this report, the model of serial type SCARA redundant robot manipulator with five degrees of freedom is presented [1]. The manipulator has P-R-R-R-P structure with each link actuated using separate armature controlled DC motor [2]. First, the kinematic model of manipulator is developed with the help of transformation matrices. The background theory of transformation matrix is also included to assist step by step derivation of the kinematic equations and further develop the concept of workspace [3]. The kinematic equations so obtained help us to generate the trajectory in Cartesian coordinate given the trajectories of individual links in joint space. Further, dynamic model of the robot manipulator has been developed using Euler-Lagrange • equation to obtain torque exerted at each manipulator joint. The dynamic model of the manipulator so derived is highly nonlinear and coupled with inertial, coriolis, centripetal and • gravitational terms appearing in the equations [4]. The control of the manipulator is realised initially using conventional PID control and further using Fuzzy Logic with independent control for each actuator. The Mamdani type FIS uses Gaussian membership function for fuzzification of the two inputs viz, position error and velocity error. The inference engine is supervised by nine linguistic fuzzy rules while the centroid method is used for defuzzification to obtain the crisp value of output control signal. Later Sugeno type FIS has also been employed to compare the performance of different types of controllers. The controllers have then been tuned using Genetic Algorithm by varying input-output scaling factors of the fuzzy system [18]. Finally, the robot model is simulated using Simulink software with Matlab functions to implement the robot dynamic equations and the kinematic equations to generate the desired trajectories. Simulation generates two different trajectories viz, helix and figure of eight to evaluate the tracking performance of the manipulator. The simulation results clearly indicate that Fuzzy controller is far superior to the conventional controllers while tracking the desired trajectories