Obstacle Avoiding Path Following based on Nonlinear Model Predictive Control using Artificial Variables This work presents a model predictive formulation for obstacle avoiding path following control for constrained vehicles. The obstacles are introduced as soft constraints in the value function, in order to maintain the convexity of state and output spaces. In this formulation, the path following and obstacle avoidance tasks may introduce local minima solutions due to their competing costs known as corner conditions. In order to address this problem, a heuristic switch in the form of additional decision variables is introduced into the cost function. The proposed solution is based on an extension of Model Predictive Control (MPC) by using Artificial Variables. An additional cost term is included in order to prevent early stops in the path following task. Simulations results considering an autonomous vehicle subject to input constraints are carried out to illustrate the performance of the proposed control strategy.

Model Predictive Control based Dynamic Path Tracking of a FourWheel Steering Mobile Robot This paper develops a new constrained model predictive control for a dynamic path tracking of an offroad mobile robot with a double steering axle. The controller is based on a dynamic model that includes wheelground lateral slippage and terrain geometry parameters. It is formulated as an optimization problem that computes at each timestep the optimal front and rear steering angles required to perform a desired path, with respect to multiple constraints, essentially the steering joint limits and the tire adhesion area bounds (i.e., pseudosliding zone limits). The capabilities of such a path tracking controller are shown and discussed through numerical simulations and experiments on a real offroad mobile robot at different speeds.