follower

One of the challenging problems of tribology is cam and follower elastohydrodynamic lubrication due to the simultaneous effect of various lubrication mechanisms. These mechanisms are transient, squeeze film, elastic deformation of contacting surfaces and variation of lubricant properties with pressure. In this paper, besides studying the mentioned factors, the effect of using a non-Newtonian lubricant such as grease is numerically investigated. The lubrication governing equations and Oswald’s grease behavior equation have been discretized using finite difference technique. The system of equation has been solved via Multi-Grid method which is an advanced iterative method in solving system of partial differential equations. The results are shown for Newtonian oil compared to grease for different cam rotational speed. Also, different grease behaviors are investigated. The results are verified by comparison of the results obtained using the famous Newton-Raphson method. Findings show that the minimum lubricant thickness as well as the maximum pressure is lower when using grease compared to the case that a Newtonian lubricant is used. In the case of Newtonian lubricant, increasing the speed results in an increase in the lubricant film thickness but it is shown that, in the case of Non-Newtonian lubricant, speed does not affect the lubricant thickness.

In this paper, the vision based formation control of a leader-follower aerial robotic system is discussed while communication failure between the robots is considered. Based on vision sensors and image processing computations, without any need to further communications, the follower robot obtains the line of sight angle and the corresponding distance from the leader. These information are used to calculate the leader speed and its path angle. Using model predictive control method, it is shown that even in the presence of communication failure between the robots, the flight formation can be controlled while the follower moves along the desired relative angle and keeps the corresponding distance. To this end, the control inputs are obtained in terms of normal and tangential acceleration of the follower. Simulation results show a good performance of the proposed model predictive controller in both ordinary, and communication failure conditions.