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Interval Markov Decision Processes with Continuous Action-Spaces 26

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Interval Markov Decision Processes with Continuous Action-Sp...

26th ACM International Conference on Hybrid systems: Computation and control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Delimpaltadakis, Giannis Lahijanian, Morteza Mazo, Manuel Laurenti, Luca Control Systems Technology Group Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Aerospace Engineering Sciences Computer Science University of Colorado Boulder BoulderCO United States Delft Center for Systems Control Mechanical Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400700330

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spaces, the action-space is assumed discrete a-priori, which is a restrictive assumption for many applications. Motivated by this, we introduce continuous-action IMDPs (caIMDPs), where the bounds on transition probabilities are functions of the action variables, and study value iteration for maximizing expected cumulative rewards. Specifically, we decompose the max-min problem associated to value iteration to |Q| max problems, where |Q| is the number of states of the caIMDP. Then, exploiting the simple form of these max problems, we identify cases where value iteration over caIMDPs can be solved efficiently (e.g., with linear or convex programming). We also gain other interesting insights: e.g., in certain cases where the action set A is a polytope, synthesis over a discrete-action IMDP, where the actions are the vertices of A, is sufficient for optimality. We demonstrate our results on a numerical example. Finally, we include a short discussion on employing caIMDPs as abstractions for control synthesis. © 2023 Copyright held by the owner/author(s).

关键词： Markov processes

Optimization with genetic algorithms of individual pitch control design with and without azimuth offset for wind turbines in the full load region

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IFAC-PapersOnLine 2023年第2期56卷 342-347页

作者： Manuel Lara Juan Garrido Jan-Willem van Wingerden Sebastian Paul Mulders Francisco Vázquez Department of Electrical Engineering and Automation University of Cordoba Campus of Rabanales 14071 Cordoba Spain Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 2628 CD Delft The Netherlands

The reduction of fatigue loadings in wind turbines to increase their lifetime has become of special interest from a control viewpoint. Individual Pitch control (IPC) is a well-known approach used to mainly mitigate periodic blade loads, and it is usually implemented with the assistance of the multi-blade coordinate (MBC) transformation, which transforms and decouples the measured blade load signals from a rotating frame into a non-rotating tilt-axis and yaw-axis. Nevertheless, these axes still show coupling between them in practical scenarios adversely affecting the system performance. Previous studies have demonstrated the benefits of including an extra tuning parameter in the MBC, the azimuth offset, in improving the performance achieved by the IPC. However, the tuning of this parameter and its real improvements that can be obtained compared to the IPC without this offset require more research. Here, two 1P+2P IPC, with and without additional azimuth offset, are designed and applied to the 5 MW reference turbine model developed by NREL using the FAST software as a simulation platform. The controller parameter tuning is formulated as an optimization problem that minimizes the blade fatigue load according to the Dirlik index and that is resolved through genetic algorithms. To fairly analyze the improvement entailed by the addition of the azimuth offset, both optimized IPC schemes, with and without azimuth offset, are compared qualitatively and quantitatively using a classical controller as the baseline case. From the simulation results, it can be stated that the optimal IPC scheme with azimuth offset compared with the IPC scheme without offset achieves improvements of around 11% in load reduction and pitch signal effort.

关键词： control system design control of renewable energy resources wind turbine control individual pitch control genetic algorithms

Analysis of Extremum Seeking control for Wind Turbine Torque controller Optimization by Aerodynamic and Generator Power Objectives

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Analysis of Extremum Seeking Control for Wind Turbine Torque...

American control Conference (ACC)

作者： S.P. Mulders A.J. Gallo M.A. Rotea Delft University of Technology Delft Center for Systems and Control Mekelweg 2 CD Delft The Netherlands Department of Mechanical Engineering University of Texas at Dallas Center for Wind Energy Richardson TX USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Wind turbines degrade over time, resulting in varying structural, aeroelastic, and aerodynamic properties. In contrast, the turbine controller calibrations generally remain constant, leading to suboptimal performance and potential stability issues. The calibration of wind turbine controller parameters is therefore of high interest. To this end, several adaptive control schemes based on extremum seeking control (ESC) have been proposed in the literature. These schemes have been successfully employed to maximize turbine power capture by optimization of the $K\omega^control$ -type torque controller. In practice, ESC is performed using electrical generator power, which is easily obtained. This paper analyses the feasibility of torque gain optimization using aerodynamic and generator powers. It is shown that, unlike aerodynamic power, using the generator power objective limits the dither frequency to lower values, reducing the convergence rate unless the phase of the demodulation ESC signal is properly adjusted. A frequency-domain analysis of both systems shows distinct phase behavior, impacting ESC performance. A solution is proposed by constructing an objective measure based on an estimate of the aerodynamic power.

关键词： Torque Power measurement Rotors Aerodynamics Generators Stability analysis Wind turbines

Data-Driven LIDAR Feedforward Predictive Wind Turbine control

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Data-Driven LIDAR Feedforward Predictive Wind Turbine Contro...

control technology and Applications (CCTA),

作者： Rogier Dinkla Tom Oomen Jan-Willem van Wingerden Sebastiaan P. Mulders Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Delft The Netherlands Department of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands

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System Identification Beyond the Nyquist Frequency: A Kernel-Regularized Approach

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arXiv 2025年

作者： van Haren, Max Smith, Roy S. Oomen, Tom Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Groene Loper 5 Eindhoven5612 AE Netherlands Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CN Netherlands

Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate outputs and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework. © 2025, CC BY-NC-ND.

关键词： Decentralized systems

Performance Analysis of Multirate systems: A Direct Frequency-Domain Identification Approach

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arXiv 2025年

作者： van Haren, Max Blanken, Lennart Oomen, Tom Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Groene Loper 5 Eindhoven5612 AE Netherlands Sioux Technologies Esp 130 Eindhoven5633 AA Netherlands Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CN Netherlands

Frequency-domain performance analysis of intersample behavior in sampled-data and multirate systems is challenging due to the lack of a frequency-separation principle, and systematic identification techniques are lacking. The aim of this paper is to develop an efficient technique for identifying the full intersample performance in the frequency-domain for closed-loop multirate systems, in particular the Performance Frequency Gain (PFG). Through local modeling techniques, aliased frequency components are effectively disentangled when identifying the PFG, which is directly facilitated by frequency-lifting the multirate system to a multivariable time-invariant representation. The developed method accurately and directly identifies the PFG in a single identification experiment. Finally, the developed method is experimentally validated on a prototype motion system, showing accurate identification of frequency-domain representations for the multirate system, including the PFG. © 2025, CC BY-NC-ND.

关键词： Frequency domain analysis

Parameter-Varying Feedforward control: A Kernel-Based Learning Approach

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arXiv 2025年

The increasing demands for high accuracy in mechatronic systems necessitate the incorporation of parameter variations in feedforward control. The aim of this paperis to develop a data-driven approach for direct learning of parameter-varying feedforward control to increase tracking performance. The developed approach is based on kernel-regularized function estimation in conjunction with iterative learning to directly learn parameter-varying feedforward control from data. This approach enables high tracking performance for feedforward control of linear parameter-varying dynamics, providing flexibility to varying reference tasks. The developed framework is validated on a benchmark industrial experimental setup featuring a belt-driven carriage. © 2025, CC BY-NC-ND.

关键词： Mechatronics

Analysis of extremum seeking control for wind turbine torque controller optimization by aerodynamic and generator power objectives

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arXiv 2024年

作者： Mulders, Sebastiaan P. Gallo, Alex J. Rotea, Mario A. Delft University of Technology Delft Center for Systems and Control Mekelweg 2 Delft2628 CD Netherlands University of Texas at Dallas Center for Wind Energy Department of Mechanical Engineering RichardsonTX75080 United States

Wind turbines degrade over time, resulting in varying structural, aeroelastic, and aerodynamic properties. In contrast, the turbine controller calibrations generally remain constant, leading to suboptimal performance and potential stability issues. The calibration of wind turbine controller parameters is therefore of high interest. To this end, several adaptive control schemes based on extremum seeking control (ESC) have been proposed in the literature. These schemes have been successfully employed to maximize turbine power capture by optimization of the Kω2-type torque controller. In practice, ESC is performed using electrical generator power, which is easily obtained. This paper analyses the feasibility of torque gain optimization using aerodynamic and generator powers. It is shown that, unlike aerodynamic power, using the generator power objective limits the dither frequency to lower values, reducing the convergence rate unless the phase of the demodulation ESC signal is properly adjusted. A frequency-domain analysis of both systems shows distinct phase behavior, impacting ESC performance. A solution is proposed by constructing an objective measure based on an estimate of the aerodynamic power. © 2024, CC BY.

关键词： Wind turbines

Online Lithium-ion Battery Modeling and State of Charge Estimation via Concurrent State and Parameter Estimation ⁎

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IFAC-PapersOnLine 2024年第15期58卷 462-467页

作者： Jimei Li Yang Wang Riccardo M.G. Ferrari Jan Swevers Feng Ding School of Internet of Things Engineering Jiangnan University Wuxi 214122 PR China MECO Research Team Dept. Mechanical Engineering KU Leuven. Flanders Make@KU Leuven 3001 leuven Belgium Delft Center for Systems and Control Delft University of Technology Mekelweg 2 2628 CD Delft The Netherlands

This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter; the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches.

关键词： Li-ion battery modeling equivalent circuit model nonlinear system identification SOC estimation least squares particle filter