Three-term Conjugate Gradient using strong wolfe line search for robotic motion / Dzul Dzaihan Dzul Dzailani

Optimization involves with finding the optimal solution of the objective function. Conjugate gradient (CG) method is known as one of best the optimization method for solving unconstrained optimization problems. CG method is implemented in various application such as robotic motion, image restoration and regression analysis. CG is classified into few classifications such as 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 computed based on number of iteration (NOI) and CPU time. These results are plotter using performance profile in order to evaluate its 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 effectively applied to the motion control of two joint planar robotic manipulators and the method has been applied to solve a practical problem of motion control