This paper presents an end-to-end framework for safe learning-based control (LbC) using nonlinear stochastic MPC and distributionally robust optimization (DRO). This work is motivated by several open challenges in LbC literature. In particular, many control-theoretic LbC methods require subject matter expertise in order to translate their own safety guarantees, often manifested as preexisting data of safe trajectories or structural model knowledge. In this paper, we focus on LbC where the controller is applied directly to a system of which it has no or extremely limited direct experience, towards safety during \textit{tabula-rasa} or ``\textit{blank slate''} model-based learning and control as a challenging case for validation. This explores the boundary of the status-quo in control theory relating to requirements for subject matter expertise. We show under basic and limited assumptions on the underlying problem, we can translate probabilistic guarantees on feasibility to nonlinear systems using results in stochastic MPC and DRO literature whose relevance we formally extend in a mathematical analysis. We also present a coupled and intuitive formulation for persistence of excitation (PoE), and illustrate the connection between PoE and applicability of the proposed method. Our case studies of vehicle obstacle avoidance and safe extreme fast charging of lithium-ion batteries reveal powerful empirical results supporting the underlying DRO theory. Our method is widely applicable within the LbC domain to, for example, airborne wind energy systems, vehicle obstacle avoidance, and energy storage systems management. It is also applicable to quantifying uncertainty beyond the LbC case.
Model Predictive Control for Safety-Critical Systems Using Taylor's Theorem with Lagrange Remainder
Boston University（波士顿大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Nanyang Technological University（南洋理工大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；University of Maryland, College Park（马里兰大学帕克分校）
Hamburg University of Technology（汉堡工业大学）；Institute of Mechanics and Ocean Engineering（力学与海洋工程研究所）
The goal of this thesis is to propose the combination of Control-Barrier-Functions (CBF) with Model-Predictive-Control (MPC) resulting in the novel Model-Predictive-Control-Barrier-Function (MPCBF). It can be shown, that the performance of the MPCBF surpasses the performance of the CBF due to the increased time horizon of the MPC. Moreover, the MPCBF was applied to a quadrotor, a system strongly in need of fast and predictive control. Using the MPCBF, the quadrotor was able to avoid obstacles, which the CBF failed to avoid due to the relative speed of the obstacle. The results of this work are…
This paper proposes a Robust Safe Control Architecture (RSCA) for safe-decision making. The system to be controlled is a vehicle in the presence of bounded disturbances. The RSCA consists of two parts: a Supervisor MPC and a Controller MPC. Both the Supervisor and the Controller are tube MPCs (TMPCs). The Supervisor MPC provides a safety certificate for an operating controller and a backup control input in every step. After an unsafe action by the operating controller is predicted, the Controller MPC takes over the system. In this paper, a method for the computation of a terminal set is propos…
Approximate non-linear model predictive control with safety-augmented neural networks
Institute for Data Science in Mechanical Engineering, RWTH Aachen University（机械工程数据科学研究所，亚琛RWTH大学）；Institute for Dynamic Systems and Control, ETH Zürich（动态系统与控制研究所，苏黎世联邦理工学院）
Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
Department of Mechanical Engineering, Clemson University（机械工程系，克莱姆森大学）
Siemens Digital Industries Software（西门子数字工业软件）；KU Leuven（鲁汶大学）；Flanders Make（法兰德斯制造研究院）
To ensure user acceptance of autonomous vehicles (AVs), control systems are being developed to mimic human drivers from demonstrations of desired driving behaviors. Imitation learning (IL) algorithms serve this purpose, but struggle to provide safety guarantees on the resulting closed-loop system trajectories. On the other hand, Model Predictive Control (MPC) can handle nonlinear systems with safety constraints, but realizing human-like driving with it requires extensive domain knowledge. This work suggests the use of a seamless combination of the two techniques to learn safe AV controllers fr…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zürich（苏黎世联邦理工学院）；University of Stuttgart（斯图加特大学）
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction.…
University of Freiburg（弗赖堡大学）；Robert Bosch GmbH（罗伯特·博世有限公司）
Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore, it generalizes CIAO's free region concept to arbitrary norms and proposes a cost function to approximate time optima…
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time. Offline (explicit) solutions for MPC attempt to alleviate real time computational challenges using either multiparametric programming or machine learning. The multiparametric approaches are typically applied to linear or quadratic MPC problems, while learning-based approaches can be more flexible and are less memory-intensive. Existing learning-based approaches offer significant speedups, but the c…
Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments
Chalmers University of Technology（查尔姆斯理工大学）；Zenseact AB（Zenseact公司）；IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）；Università di Modena e Reggio Emilia（摩德纳-雷焦艾米利亚大学）
From Data to Safe Mobile Robot Navigation: An Efficient and Modular Robust MPC Design Pipeline
department of Cognitive Robotics, Delft University of Technology（认知机器人学系，代尔夫特理工大学）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（信息学、机器人学与自动化研究所，布拉格技术大学）；Institute for Dynamic Systems and Control, ETH Zürich（动态系统与控制研究所，苏黎世联邦理工学院）
Maximal Controlled Invariant-MPC: Enhancing Feasibility and Reducing Conservatism through Terminal CBF Constraint in Safety-Critical Control
Graduate Student, Daniel Guggenheim School of Aerospace Engineering（丹尼尔·古吉恩航空航天工程学院研究生）；Assistant Professor, Director, Aerospace Robotics Lab, Daniel Guggenheim School of Aerospace Engineering（丹尼尔·古吉恩航空航天工程学院助理教授、主任、航空航天机器人实验室）
Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environments
Continental Automotive Technologies GmbH, Germany（德国大陆汽车技术有限公司）；Technical University of Berlin, Germany（柏林技术大学）；Free University of Berlin, Germany（柏林自由大学）
Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions
D. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology（佐治亚理工学院D.Guggenheim航空航天工程学院）；Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空与航天工程系）
Safe On-Orbit Dislodging of Deployable Structures via Robust Adaptive MPC
Department of Electrical and Computer Engineering, The University of New Mexico（电气与计算机工程系，新墨西哥大学）；Department of Mechanical Engineering, The University of New Mexico（机械工程系，新墨西哥大学）
This paper presents a novel, safe control architecture (SCA) for controlling an important class of systems: safety-critical systems. Ensuring the safety of control decisions has always been a challenge in automatic control. The proposed SCA aims to address this challenge by using a Model Predictive Controller (MPC) that acts as a supervisor for the operating controller, in the sense that the MPC constantly checks the safety of the control inputs generated by the operating controller and intervenes if the control input is predicted to lead to a hazardous situation in the foreseeable future inva…
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（南加州大学明希德电气与计算机工程系）；Department of Aeronautics and Astronautics, Stanford University（斯坦福大学航空与航天系）；Electrical and Computer Engineering Department, New York University（纽约大学电气与计算机工程系）
D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
Paderborn University（帕德博恩大学）；Illinois Institute of Technology（伊利诺伊理工学院）；California Institute of Technology（加州理工学院）；Technical University of Munich（慕尼黑工业大学）
