Trajectory control of a laparoscopic 3-PUU parallel manipulator based on neural network in SIMSCAPE SIMULINK environment

Abstract

This paper presents the modeling and control process of recently developed manipulator with 3 limbs having prismatic-universal-universal (3-PUU) joints. It has 3 degrees of freedom (3-DOF), consisting of 2 rotational DOFs and 1 translational DOF (2R1T). To avoid the computational complexity of solving the manipulator’s kinematics in real-time application, two artificial neural networks (ANNs) are trained to estimate the forward and inverse kinematics solutions. Training and testing results show that the developed ANNs have great prediction capabilities. The manipulator’s dynamic model is deduced using MATLAB Simscape environment. Control schemes are investigated starting with motor control, first using PID controller, and then adding feed-forward control which greatly improved the motors’ response. Closed-loop trajectory control based on Cartesian space feedback of the manipulators’ position and orientation and inverse kinematics ANN is then studied. The closed-loop control scheme can enhance the system’s performance, eliminating the error resulting from any change in the manipulator’s actual model due to manufacturing or assembly defects. Simulation results of a defected manipulator model show that the closed-loop control scheme improved the manipulator’s trajectory tracking capability, reducing the z-axis position error by 89.23% and the orientation error by 86.76% and 82.83% about x-axis and y-axis directions respectively.