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Necessary Optimality Conditions for dynamic systems Described by Ordinary Stochastic Difference Equations

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Necessary Optimality Conditions for Dynamic Systems Describe...

International Conference on Problems of Cybernetics and Informatics (PCI)

作者： Kamil Mansimov Rashad Mastaliyev Laboratory of Methods of Control of Complex Dynamic Systems Institute of Control Systems Baku Azerbaijan Department of Mathematics and Informatics Azerbaijan University Laboratory of Methods of Control of Complex Dynamic Systems Institute of Control Systems Baku Azerbaijan

The stochastic optimal control problem of an ordinary non-linear discrete stochastic system is considered. The quality of control is estimated by the mathematical expectation of the Boltz type functional to be minimized. Considering that the control domain is open, first and second orders stochastic type necessary optimality criterions are formulated and proved, including stochastic Euler equation.

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Predictive stability filters for nonlinear dynamical systems affected by disturbances

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IFAC-PapersOnLine 2024年第18期58卷 200-207页

作者： Alexandre Didier Andrea Zanelli Kim P. Wabersich Melanie N. Zeilinger Institute for Dynamic Systems and Control ETH Zürich 8092 Zürich Switzerland Contributed equally to this paper

Predictive safety filters provide a way of projecting potentially unsafe inputs, proposed, e.g. by a human or learning-based controller, onto the set of inputs that guarantee recursive state and input constraint satisfaction by leveraging model predictive control techniques. In this paper, we extend this framework such that in addition, robust asymptotic stability of the closed-loop system can be guaranteed by enforcing a decrease of an implicit Lyapunov function which is constructed using a predicted system trajectory. Differently from previous results, we show robust asymptotic stability with respect to a predefined disturbance set on an extended state consisting of the system state and a warmstart input sequence. The proposed strategy is applied to an automotive lane keeping example in simulation.

关键词： Robust Model Predictive control Stability and Recursive Feasibility Automotive

From Data to Predictive control: A Framework for Stochastic Linear systems with Output Measurements

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arXiv 2024年

作者： Balim, Haldun Carron, Andrea Zeilinger, Melanie N. Kohler, Johannes Institute for Dynamic Systems and Control ETH Zurich ZurichCH-8092 Switzerland

We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-time stochastic linear systems with output measurements and provides a principled design of a predictive controller based on data. The framework starts with a parameter identification method based on the Expectation-Maximization algorithm, which incorporates pre-defined structural constraints. Additionally, we provide an asymptotically correct method to quantify uncertainty in parameter estimates. Next, we develop a strategy to synthesize robust dynamic output-feedback controllers tailored to the derived uncertainty characterization. Finally, we introduce a predictive control scheme that guarantees recursive feasibility and satisfaction of chance constraints. This framework marks a significant advancement in integrating data into robust and predictive control schemes. We demonstrate the efficacy of D2PC through a numerical example involving a 10-dimensional spring-mass-damper system. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

Adaptive Economic Model Predictive control for linear systems with performance guarantees

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Adaptive Economic Model Predictive Control for linear system...

IEEE Conference on Decision and control

作者： Maximilian Degner Raffaele Soloperto Melanie N. Zeilinger John Lygeros Johannes Köhler Automatic Control Laboratory ETH Zürich Institute for Dynamic Systems and Control ETH Zürich

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters. The proposed formulation combines a certainty equivalent economic MPC with a simple least-squares parameter adaptation. For the resulting adaptive economic MPC scheme, we derive strong asymptotic and transient performance guarantees. We provide a numerical example involving building temperature control and demonstrate performance benefits of online parameter adaptation.

关键词： Economics Linear systems Adaptation models Adaptive systems Uncertainty Predictive models Temperature control Numerical models Transient analysis Predictive control

Robust Hybrid Navigation Unit for Urban Air Mobility Scenarios 43

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Robust Hybrid Navigation Unit for Urban Air Mobility Scenari...

43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Corraro, Gianluca Cuciniello, Giovanni Garbarino, Luca Ciniglio, Umberto Corraro, Federico Flora, Andrea Senatore, Roberto Pizzarulli, Andrea Guidance Navigation and Control Dept. CE Capua Italy Flight Dynamic and Simulation Dept. CE Capua Italy On-board Systems for Navigation Dept. CE Capua Italy Civitanavi Systems Guidance Navigation & Control Applications Dept. FM Pedaso Italy Inertial Algorithm and Simulation Civitanavi Systems FM Pedaso Italy Civitanavi Systems FM Pedaso Italy Civitanavi Systems FM Pedaso Italy

ISBN: (纸本)9798350349610

Urban Air Mobility (UAM) includes new aeronautical applications encompassing the future on-demand urban human air transportation (e.g. air-taxi) and unmanned aerial system operations for goods delivery and public safety in urban, sub-urban and inter-city areas. One of the key technology enablers of UAM implementation is related to the availability of robust and resilient positioning, navigation and timing measurements. In this context, the Global Navigation Satellite systems (GNSS) is in principle able to provide valid technological solutions to above required performances that are threatened in dense urban environments. In this framework, the present work describes a hybrid navigation unit for UAM applications implementing all the algorithms for a tightly coupled sensor fusion between a dual frequency multi constellation GNSS receiver, an inertial measurements unit and barometric altitude from an air data computer, together with related autonomous fault detection and exclusion of GPS/Galileo/BeiDou satellites and estimation of navigation solution integrity/accuracy. Such prototype has been validated through real-time hardware-in-the-loop simulations and in-flight demonstrations in reproduced UAM scenarios. © 2024 IEEE.

关键词： Global positioning system

Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning

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arXiv 2023年

作者： Bi, Thomas D'Andrea, Raffaello Institute for Dynamic Systems and Control ETH Zurich Switzerland

Motivated by the challenge of achieving rapid learning in physical environments, this paper presents the development and training of a robotic system designed to navigate and solve a labyrinth game using model-based reinforcement learning techniques. The method involves extracting low-dimensional observations from camera images, along with a cropped and rectified image patch centered on the current position within the labyrinth, providing valuable information about the labyrinth layout. The learning of a control policy is performed purely on the physical system using model-based reinforcement learning, where the progress along the labyrinth's path serves as a reward signal. Additionally, we exploit the system's inherent symmetries to augment the training data. Consequently, our approach learns to successfully solve a popular real-world labyrinth game in record time, with only 5 hours of real-world training data. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Robust Path-following controller for Multirotor Vehicles in Uncertain Wind Conditions

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Robust Path-following Controller for Multirotor Vehicles in ...

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Bullock, John L. Trujillo, Anna C. Cheng, Sheng Hovakimyan, Naira Dynamic Systems and Control Branch NASA Langley Research Center HamptonVA23681 United States Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper presents simulations of L1 adaptive controller on a multirotor vehicle for compensation of convective winds. There are increasing efforts to utilize unmanned vehicles for fighting wildfires, but such vehicles need to be able to compensate for convective updrafts and other uncertainties (e.g., wind shear and turbulence) caused by the wildfire. Other application domains, such as urban air mobility, also must account for convective or uncertain winds present in urban microclimates. Geometric controllers have great tracking performance properties in environments without uncertainties, but the tracking performance degrades when wind disturbances and external loads are present in the environment. Introducing L1 adaptive control into the controller architecture compensates for these wind disturbances and reduces the tracking error caused by such disturbances. The controller architecture is implemented on a small quadrotor. The simulations demonstrate the advantages of the L1 adaptive augmentation for a sample figure-eight trajectory while encountering unknown variable wind conditions with an external load. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Adaptive control systems

Gaussian processes for dynamics learning in model predictive control

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arXiv 2025年

作者： Scampicchio, Anna Arcari, Elena Lahr, Amon Zeilinger, Melanie N. Institute for Dynamic Systems and Control ETH Zurich Leonhardstrasse 21 Zurich8092 Switzerland

Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based model predictive control in a variety of applications over the last years. However, despite its evident practical effectiveness, there are still many open questions when attempting to analyze the associated optimal control problem theoretically and to exploit the full potential of Gaussian process regression in view of safe learning-based control. The contribution of this review is twofold. The first is to survey the available literature on the topic, highlighting the major theoretical challenges such as (i) addressing scalability issues of Gaussian process regression;(ii) taking into account the necessary approximations to obtain a tractable MPC formulation;(iii) including online model updates to refine the dynamics description, exploiting data collected during operation. The second is to provide an extensive discussion of future research directions, collecting results on uncertainty quantification that are related to (but yet unexploited in) optimal control, among others. Ultimately, this paper provides a toolkit to study and advance Gaussian process-based model predictive control. © 2025, CC BY.

关键词： Predictive control systems

Predictive stability filters for nonlinear dynamical systems affected by disturbances

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arXiv 2024年

作者： Didier, Alexandre Zanelli, Andrea Wabersich, Kim P. Zeilinger, Melanie N. Institute for Dynamic Systems and Control ETH Zürich Zürich8092 Switzerland

关键词： Closed loop systems