A Causal Discrete-time Estimator-Predictor for Unicycle Trajectory Tracking

This paper proposes a nonlinear sampled-data (SD) control approach for the trajectory tracking of a class of nonlinear differentially at systems that encompass the unicycle, which is widely used in the context of unmanned aerial vehicle (UAV). The nonlinear SD control method relies on the a atness property for the generation of appropriate trajectories, with the design of one-step predictive control laws, and on controller discretization by means of an averaging-like method. The causality problem that might arise in the implementation is avoided by using an estimator based on numerical integration techniques of suf a ciently high order. Numerical simulations show that the proposed SD control law offers the best closed-loop performance when compared with nonlinear direct digital design for the trajectory tracking of a unicycle. Furthermore, the results show that the proposed scheme is less sensitive to quantization errors arising with a nite word length and xed point arithmetic microprocessors than nonlinear direct digital design. The SD control relies on closed-form integrability of the UAV.