A collision avoidance system at intersections using Robust Model Predictive Control

Abstract

Deadbeat Robust Model Predictive Control (DRMPC) is introduced as a new approach of Robust Model Predictive Control (RMPC) for linear systems with additive disturbances. Its main idea is to completely extinguish the effect of the disturbances in the predictions within a small number of time steps, called the deadbeat horizon. To this end, explicit deadbeat input sequences are calculated for the vertices of the disturbance set. They generalize to a nonlinear disturbance feedback policy for all disturbances by means of a barycentric function. Similar to previous approaches, this disturbance feedback policy can be either part of the online optimization (Online DRMPC) or pre-calculated during the design phase of the controller (Offline DRMPC). The main advantage over all other RMPC approaches is that no Robust Positive Invariant (RPI) set has to be calculated, which is often intractable for systems with higher dimensions. Nonetheless, for Online DRMPC and Offline DRMPC recursive feasibility and input-to-state stability can be guaranteed. A small numerical example compares the two versions of DRMPC and demonstrates that the performance of DRMPC is competitive with other state-of-the-art RMPC approaches. Its main advantage is its easy extension to linear time-varying (LTV) and linear parameter-varying (LPV) systems.

Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors

“…MPC-based controllers have been widely used in the literature. A centralized MPC based optimization algorithm for intersection clearance is used to optimize the longitudinal motion of a vehicle by researchers at University of California Berkeley [17]. Rodrigues de Campos et al evaluate centralized control algorithm vs decentralized control algorithm for traffic coordination at intersections [18].…”

“…In order to make MPC-based controllers perform well even under adversaries, robust MPC techniques are implemented. A min-max MPC approach has been used to make a centralized controller robust to uncertainties in MDV behavior [17] where as Chen et al use min-max MPC to create a centralized controller robust to communication and actuator delays [9]. Authors Gao et al in [11] propose the use of a robust controller to counter both parameter uncertainties and uniform communication delay.…”

Fast Trajectory Planning for Automated Vehicles Using Gradient-Based Nonlinear Model Predictive Control

“…A first approach [3] exploited the decomposition of the planning task into a lateral and longitudinal motion profile search. Recent work [4], [5], [6] successfully implemented model predictive control (MPC) algorithms by a combined consideration of a vehicle's nonlinear dynamics. The nonholonomic character makes the task of trajectory planning even more complicated.…”

Remote control of automated vehicles over unreliable channels

“…A practical approach to solve such problems is model predictive control (MPC), which was also applied in [4] to obtain optimized trajectories for vehicles in the surroundings of an intersection, based on a centralized approach. A robust MPC problem was studied in [5] in order to control an ego vehicle in an intersection, avoiding collision with a target vehicle driven by unknown, bounded control actions. Finally, [6] takes into account a time-slot assignment approach for collision avoidance, relying on wireless communication.…”