THREE-TERM CONJUGATE GRADIENT METHOD USING STRONG WOLFE LINE SEARCH FOR ROBOTIC MOTION

Abstract

Optimization involves finding the optimal solution of the objective function. One of the best optimization methods for solving unconstrained optimization problems is Conjugate gradient (CG) method [1]. CG method is implemented in various applications such as robotic motion, image restoration and regression analysis. CG methods are classified into several categories, including scaled, three-term and hybrid CG methods. This research focuses on the performance of three term CG method (TTCG) under strong Wolfe line search and its applicability in robotic motion control. The performance of four TTCG methods, TTLAMR, TTRMIL, TTSMAR, and TTKMAR coefficients are tested using 15 standard test functions with different initial points and variables ranging from 2 to 10,000. The numerical results are evaluated based on the number of iterations (NOI) and CPU time. These results are plotted using a performance profile to evaluate efficiency and robustness. Numerically, TTLAMR outperforms other methods by solving all test functions and it is followed by TTSMAR (99.38%), TTRMIL (97.84%), and TTKMAR (93.83%). Lastly, TTLAMR is implemented in robotic motion control. It shows that TTLAMR is able to be applied to the motion control of two joint planar robotic manipulators.