Forward Kinematics of the Stewart Parallel Manipulator Using Machine Learning

Abstract

Stewart parallel manipulator is well known for its superiority in achieving better stiffness, accurate motion with precise positioning, robust mechanism, high payload capacity, etc. It is widely used in various applications such as flight simulators, satellite dish positioning, hexapod telescope, medical surgery, simulation of earthquakes, etc. It is important to note that the inverse kinematics solution of the Stewart platform can be determined easily with the help of an analytical solution, whereas forward kinematics is intractable analytically. Therefore, in this work, an attempt is made to solve the forward kinematics problem of the Stewart platform using the soft-computing-based technique. A multi-layer feed-forward neural network with one hidden layer is trained after utilizing different metaheuristic optimizers, namely Particle Swarm Optimization (PSO), Modified Chaotic, Invasive Weed Optimization (MCIWO), and Teachers’ Learning-Based Optimization (TLBO) methodologies to solve the forward kinematics of the Stewart platform. Further, a detailed analysis is conducted on the results obtained by these methods, namely PSO-NN, MCIWO-NN and TLBO-NN. The dataset for training the NN is generated by using the solution of inverse kinematics.