Robust Control for a 3DOF Articulated Robotic Manipulator Joint Torque under Uncertainties

Abstract

This research work emphasizes on design of a robust control for a 3DOF robotic manipulator under uncertainties. The plant model was achieved using the independent joint method and the uncertainty problem was addressed by designing a robust controller using H-Infinity synthesis which was compared with PID. This was achieved with algorithms implemented in MATLAB. The H-Infinity controller recorded 0dB, while PID controller recorded 0.117dB and 0.061dB for joints I and II respectively in Complementary Sensitivity (T) graph at low frequencies. H-Infinity controller achieved better disturbance rejection characteristics with sensitivity (S) graph recording peak sensitivity of 0.817dB and 1.79dB at joints I and II respectively than PID controller which achieved 3dB and 1.86dB at joints I and II respectively. H-Infinity controller achieved better noise rejection characteristics with T graph recording lower gains at joints I and II respectively at high frequencies than PID controller which recorded higher gains at joints I and II respectively. Thus, it was concluded that the H-Infinity controller achieved better performance and stability robustness characteristics for the joint torque control than the PID.

Modelling and simulation of a prismatic: prismatic robotic arm manipulator

“…Double prismatic robotic manipulators are extensively utilized in a variety of industries due to their ability of providing linear motion in two directions. This enables precision and flexibility control of the manipulator's end-effector [9,16,[66][67][68][69][70][71][72]. One of the main benefits of double prismatic joints is their capability to support heavy loads, which makes them very well suited for handling heavy materials in manufacturing industries [73][74][75][76].…”

“…The need for complicated robotic manipulators is rising because of their capabilities to alter the environment precisely with flexibility. To achieve this robust controller [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] and good estimation techniques, shall be used . Moreover, among the many usages of the of artificial intelligence (AI) [56][57][58][59][60] the integration of it in robotic manipulators enhances their capabilities by enabling intelligent behavior, adaptive control, computer vision, and learning algorithms for improved performance and precision in various industrial applications [2,31,39,41,[61][62][63][64][65].…”