Experimental modeling and optimal control of a multi-degree of freedom hydraulic robot manipulator

In this study, experimental modeling and optimal control of a multi degree of freedom hydraulic robot manipulator has been realized. The hydraulic robot manipulator has two types of movement: lifting-lowering and extension-pulling. These movements fourteen in total are driven by seven drive elements. Experimental modeling of these fourteen different movements were performed and transfer functions were obtained. PID controller designs were made for optimal control of the system using the transfer functions obtained and PID controller gains were tuned. The transfer function of fourteen different movements is defined by MATLAB System Identification Toolbox using the data obtained experimentally. Using the transfer functions obtained, Genetic Algorithm, Particle Swarm Optimization, The Bees Algorithm, Cuckoo Bird Algorithm and Big Bang Big Crunch optimization methods have been minimized the cost function of the system, which was originally determined according to the closed loop step response. In this study, a hybrid algorithm using Big Bang Big Crunch and Hooke Jeeves algorithm is proposed. The proposed algorithm and other optimization algorithms were compared and the results obtained were presented graphically.