Optimizing Nonlinear Lateral Control for an Autonomous Vehicle

The lack of general algorithms for the control of nonlinear systems is a generalized problem, especially when attempting to stabilize systems such as ground vehicles, which have uncertainties and are usually linearized under the assumption of small angles. To solve this problem, in this work, the implementation of a suboptimal discrete control is developed to stabilize an autonomous automobile. We assume the system is affine for the optimization procedure of finite horizon that allows us to find a solution while avoiding solving the Ricatti-type equation, commonly encountered in this kind of algorithm. This procedure is applied to the dynamical model of the lateral displacement and orientation errors of the vehicle that was discretized through the method of Euler. These nonlinear models discretized to compute a bounded control. The control is tested in different simulated scenarios to show the efficiency of the system for solving typical tasks for the path planning of an autonomous vehicle.