  Modern railway traction systems are often equipped with anti-slip control
strategies to comply with performance and safety requirements. A certain amount
of slip is needed to increase the torque transferred by the traction motors
onto the rail. Commonly, constant slip control is used to limit the slip
velocity between the wheel and rail avoiding excessive slippage and vehicle
derailment. This is at the price of not fully utilizing the train's traction
and braking capabilities. Finding the slip at which maximum traction force
occurs is challenging due to the non-linear relationship between slip and
wheel-rail adhesion coefficient, as well as to its dependence on rail and wheel
conditions. Perturb and observe (P\&O) and steepest gradient (SG) methods have
been reported for the Maximum Adhesion Tracking (MAT) search. However, both
methods exhibit weaknesses. Two new MAT strategies are proposed in this paper
which overcome the limitations of existing methods, using Fuzzy Logic
Controller (FLC) and Particle Swarm Optimization (PSO) respectively. Existing
and proposed methods are first simulated and further validated experimentally
using a scaled roller rig under identical conditions. The results show that the
proposed methods improve the traction capability with lower searching time and
oscillations compared to existing solutions. Tuning complexity and
computational requirements will also be shown to be favorable to the proposed
methods.
