Intelligent control of a three-DOF planar underactuated manipulator

Recently, computational intelligence has been applied extensively in control engineering, especially for systems that cannot easily be controlled by conventional means. In this article, attention is paid to the control of a three-DOF planar underactuated manipulator, also known as the three-link gymnastic robot, by utilizing a neural network (NN) and a genetic algorithm (GA). In an attempt to make the problem more analogous to human gymnastics, constraints are applied to the joint angles. With different swing-up timings, the performance of the proposed controller is investigated and control simulations are performed. Numerical simulations show that the neurocontroller is able to control the system effectively within the constraints and given timings.