ADAPTIVE MODEL PREDICTIVE CONTROLLER FOR TRAJECTORY TRACKING AND OBSTACLE AVOIDANCE ON AUTONOMOUS VEHICLE
Keywords:Autonomous vehicle, adaptive MPC controller, trajectory tracking, collisions avoidance
AbstractAdvancement in active steering technology is vital as the autonomous vehicle technology is preparing to enter the commercialization phase. Accurate trajectory tracking and collision free motion have become an active topic being discussed in research field recently. During an emergency obstacle avoidance manoeuvre conditions, tyre force saturation can easily happened when availability of lateral tyre forces is limited by the law of tyre friction circle. This greatly affects the trajectory tracking performance of the vehicle. Existing controllers such as generic model predictive controller (MPC) and geometric controller (Stanley) need a proper gain tuning to cope with this condition. This is due to the control gains were determined by trial and error basis via linearization process at a certain targeted speed. Therefore, the control performance is limited considering the presence of speed variation as well as extreme manoeuvre trajectory. This paper proposes an Adaptive Model Predictive Controller (MPC) controller to solve aforementioned issues. First, optimized weighting gains for the steering control were obtained using PSO algorithm. The optimised weighting gains were then scheduled into the proposed Model predictive Controller via a look-up table strategy. In this work, the proposed adaptive MPC controller was designed by using the linearization of the 7 degree-of-freedom (DOF) non-linear vehicle model. Here, the linearized model for controller design was update based on the instantaneous longitudinal speed of the vehicle system plant.
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