Model predictive control for autonomous ground vehicles: a review

Abstract

This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

Learning‐based model predictive control for improved mobile robot path following using Gaussian processes and feedback linearization

“…MPC has been widely used for mobile robot path following applications (Prado et al, 2020; Yu et al, 2021). MPC uses a vehicle model to predict future path‐following errors over a finite prediction time horizon given a finite sequence of computed control inputs.…”

“…The process models of wheeled robots, whether they are kinematic (Tang et al, 2020), dynamic (Prado et al, 2020), or even learned vehicle models (Kabzan et al, 2019), are nonlinear and result in a non‐convex MPC cost function that must be optimized using computationally expensive nonlinear solving techniques (Amer et al, 2016; Ostafew et al, 2016). MPC is being actively researched in the path following control of wheeled robots; see Yu et al (2021) for a comprehensive review. This section will focus on reviewing MPC methods that combine FBL or/and (Gaussian process) learning‐based methods for the path following control of mobile robots.…”

An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

“…As for detecting and recovering from sensor and actuator failures [16], control theorists have proposed a number of approaches, in which detection often relies on state estimation [2] and deviation/bias measurement and analysis [3], which are easy to understand and work well for detecting different degrees of particular types of failure, but do not extend well to detecting different failures that can appear similar, thus making learning-based approaches more appealing for our case study [17]. The control techniques used for correction include adaptive control [18] and model predictive control (MPC), which has been shown extensively to produce safe motion planning under degraded conditions [19]. In this work, we bridge the gap between learning and control based approaches by designing an explainable Decision Tree (DT)based monitor for learning-based decision-making without the use of black boxes and integrating MPCs designed to keep the system safe under different sensor and actuator faults and disturbances.…”

“…Each controller c i ∈ C is tuned appropriately based on the dynamics of each degraded system. In this work, we use standard MPC for trajectory tracking [19] since it inherently provides predictions for the robot's future states x p (t) = [x y θ] ⊤ , which will be compared with the observed state x(t) of the robot at runtime to facilitate failure detection and will be used for the reachability analysis performed in Sec. IV-C.…”

Coalitional Distributed Model Predictive Control Strategy for Vehicle Platooning Applications

“…One of the recent interests in the control community is focused on developing advanced control techniques in the framework of autonomous vehicles. Thus, considerable efforts were made to increase the efficiency and to improve the collaboration through the connectivity between multiple autonomous vehicles, using well established control strategies, such as Model Predictive Control (MPC) or Distributed Model Predictive Control (DMPC) [1]. Among these strategies, vehicle platooning of connected and autonomous vehicles has increased potential [2].…”