Modelling and Force Tracking Control for Newly Type Configuration of Magneto-rheological Damper

The objective of this paper is to model hysteresis behaviour of new MR damper configuration by using nonparametric model approaches. The approaches are nonparametric linearised data-driven (NPLDD) single input model, non-parametric linearised data-driven (NPLDD) double input model, and simple polynomial model. The modelling is developed to ensure the force of MR damper is tracked to any input force. The NPLDD model is developed based on look-up table while the polynomial model is developed based on curve fitting from the experimental results and consists of a pair of subsystems namely positive and negative acceleration which corresponds to the upper and lower curves. From the simulation results, the NPLDD double input model shows better performance in describing non-linear hysteresis behaviour of the MR damper compared with others. By using the NPLDD model, a force tracking based on PI controller has been developed. It is verified that the NPLDD model together with the PI control strategy has the capability to track the desired damping force well

Fuzzy Optimal Swarm of Autonomous Aircrafts for Target Determination and Convergence Control System

The thesis project proposes analytical and theoretical algorithms for a networked swarm of autonomous vehicles, such as those used in planet exploration, and to be used in target location determination and convergence, an algorithm of this type could be used in an Autonomous Stratospheric Aircraft (ASA), thus having the possibility of being used for the exploration of a planet as well as many other applications. Upon locating an unknown location of a specified target, the algorithm would then swarm and eventually converge upon the location. There are two similar, but fundamentally different algorithms proposed in this project. These algorithms are capable of locating and converging upon multiple targeted locations simultaneously. This project is inspired by the current thought of NASA in the search of life on Mars, which is the Water where the targeted location would be the targeted source of water. These algorithms make use of combining a modified Particle Swarm Optimization algorithm with fuzzy variables for increased intelligence