This study presents a nonlinear model predictive control (NMPC) formulation for preview-based traction control, which uses the information on the expected tire-road friction coefficient ahead to enhance the wheel slip control performance, in the context of connected vehicles with V2X features. Proof-of-concept experiments on an electric vehicle prototype highlight the real-time capability of the controller, and the wheel slip control performance improvement brought by the tire-road friction coefficient preview. Finally, an experimentally validated simulation model is used in sensitivity analyses, to evaluate the performance benefit of the preview-based controller for different dynamic characteristics (e.g., time constant and pure time delays) of the electric powertrains.
This paper proposes a stabilising model predictive control (MPC) scheme with preview information of disturbance for nonlinear systems. The proposed MPC algorithm is able to not only reject disturbance by making use of disturbance preview information as necessary, but also take advantage of the disturbance if it is good for a control task. This is realised by taking into account both the task (e.g. reference trajectory) and disturbance preview in the prediction horizon when performing online optimisation. Conditions are established to ensure recursive feasibility and stability under the disturb…
Coasting has been widely used in the eco-driving guidelines to reduce fuel consumption by profiting from kinetic energy. However, the comprehensive comparison between different coasting strategies and online performance of the eco-coasting strategy using road grade preview are still unclear because of the oversimplification and the integer variable in the optimal control problems. Herein, two different coasting strategies (fuel cut-off and engine start/stop) are proposed to reveal the potential benefit of eco-coasting using the road grade preview. Engine drag torque and energy cost used for en…
KTH Royal Institute of Technology（瑞典皇家理工学院）；University of California Berkeley（加州大学伯克利分校）；Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）
In this paper, we address the problem of motion planning and control at the limits of handling, under locally varying traction conditions. We propose a novel solution method where traction variations over the prediction horizon are represented by time-varying tire force constraints, derived from a predictive friction estimate. A constrained finite time optimal control problem is solved in a receding horizon fashion, imposing these time-varying constraints. Furthermore, our method features an integrated sampling augmentation procedure that addresses the problems of infeasibility and sensitivity…
Deep Koopman risk-preview supervised LTV-MPC for direct yaw moment control of distributed drive electric vehicles
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Department of Automotive Engineering, Hanyang University（汉阳大学汽车工程系）；School of Vehicle Engineering, Chongqing University of Technology（重庆理工大学车辆工程学院）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；EarthSense Inc.（EarthSense公司）；DEVCOM Army Research Lab（DEVCOM陆军研究实验室）
We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural…
This paper proposes a fuel-economical distributed model predictive control design (Eco-DMPC) for a homogenous heavy-duty truck platoon. The proposed control strategy integrates a fuel-optimal control strategy for the leader truck with a distributed formation control for the following trucks in the heavy-duty truck platoon. The fuel-optimal control strategy is implemented by a nonlinear model predictive control (NMPC) design with an instantaneous fuel consumption model. The proposed fuel-optimal control strategy utilizes the preview information of the preceding traffic to achieve the fuel-econo…
University of California Irvine（加州大学欧文分校）；Expedia Group（亿客行集团）；Texas A&M University（德州农工大学）
We study the problem of online learning in predictive control of an unknown linear dynamical system with time varying cost functions which are unknown apriori. Specifically, we study the online learning problem where the control algorithm does not know the true system model and has only access to a fixed-length (that does not grow with the control horizon) preview of the future cost functions. The goal of the online algorithm is to minimize the dynamic regret, defined as the difference between the cumulative cost incurred by the algorithm and that of the best sequence of actions in hindsight. …
Simultaneous vibration control and energy harvesting of vehicle suspensions has attracted great research interests over the past decades. However, existing frameworks tradeoff suspension performance for energy recovery and are only responsive to narrow-bandwidth vibrations. In this paper, a new energy-regenerative vibration absorber (ERVA) using a ball-screw mechanism is investigated. The ERVA system is based on a rotary electromagnetic generator with adjustable nonlinear rotational inertia which passively increases the moment of inertia as the vibration amplitude increases. This structure is …
Recently, vision-based control has gained traction by leveraging the power of machine learning. In this work, we couple a model predictive control (MPC) framework to a visual pipeline. We introduce deep optical flow (DOF) dynamics, which is a combination of optical flow and robot dynamics. Using the DOF dynamics, MPC explicitly incorporates the predicted movement of relevant pixels into the planned trajectory of a robot. Our implementation of DOF is memory-efficient, data-efficient, and computationally cheap so that it can be computed in real-time for use in an MPC framework. The suggested Pix…
