  A high-gain observer-based cooperative deterministic learning (CDL) control
algorithm is proposed in this chapter for a group of identical unicycle-type
unmanned ground vehicles (UGVs) to track over desired reference trajectories.
For the vehicle states, the positions of the vehicles can be measured, while
the velocities are estimated using the high-gain observer. For the trajectory
tracking controller, the radial basis function (RBF) neural network (NN) is
used to online estimate the unknown dynamics of the vehicle, and the NN weight
convergence and estimation accuracy is guaranteed by CDL. The major challenge
and novelty of this chapter is to track the reference trajectory using this
observer-based CDL algorithm without the full knowledge of the vehicle state
and vehicle model. In addition, any vehicle in the system is able to learn the
knowledge of unmodeled dynamics along the union of trajectories experienced by
all vehicle agents, such that the learned knowledge can be re-used to follow
any reference trajectory defined in the learning phase. The learning-based
tracking convergence and consensus learning results, as well as using learned
knowledge for tracking experienced trajectories, are shown using the Lyapunov
method. Simulation is given to show the effectiveness of this algorithm.
