A ball and beam mechanism is widely utilized in laboratory experiments to demonstrate the behavior of more complex systems. In this research, the phenomena such as nonlinear frictions, dead-zone and time-delay in the ball and beam mechanism's mathematical model is investigated. The following procedures are taken to construct a credible mathematical model of the system for this purpose. Firstly, the ball and beam mechanism's mathematical model, which includes different probable physically meaningful nonlinearities, is simulated using MATLAB\Simulink. Then, the Particle Swarm Optimization (PSO) algorithm is coded to determine the exact nonlinear model of a ball and beam system using the experimental data. Third, the accuracy of the results obtained from the PSO algorithm is tested using the hypothesis test and the confidence interval test. According to the statistical tests, the PSO algorithm is highly accurate in determining the parameters of the actual model of the system.

Industrial robots are traditionally programmed by hard-coding the desired motion into them. That approach, however, costs significant time and effort and shows little to no promise in transferring human skills to robots. Programming by demonstration (PbD) is an alternative approach that allows robots to learn tasks from demonstrations. Because of its several advantages over the traditional method, PbD is particularly suited for tasks encountered in assembly operations, the most typical of which is the peg-in-hole task. A successful PbD implementation for a peg-in-hole task requires that the peg should still be inserted into the hole even under situations that are not encountered during the demonstrations. Previous research in the field shows that the success rate of a peg-in-hole task under such cases varies greatly. In this study, we use a UR5 manipulator to experimentally investigate how the success rate of a peg-in-hole task changes with respect to the novelty of the task, quantified in terms of the distance of the hole to its original position. It is found that the success ratio decreases as the novelty of the task increases. To increase the performance, the use of strategies that alter the robot's motion dynamically in the run time is suggested for future work.