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Predictive control with Terminal Costs Based on Online Learning Using Value Iteration

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Predictive Control with Terminal Costs Based on Online Learn...

European control Conference (ECC)

作者： Francisco Moreno-Mora Stefan Streif Laboratory for Automatic Control and System Dynamics Technische Universitat Chemnitz Chemnitz Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this work, a predictive controller that uses online-learned terminal costs is proposed. The learned costs are based on Approximate Dynamic Programming (ADP), specifically, Value Iteration (VI), an ADP technique that aims to iteratively determine the optimal value function of an optimal control problem. With this, we aim to improve infinite-horizon controller performance. Instead of performing an offline iteration over the whole state space, we consider a local update law which is executed online and reduces the computational burden. We first extend results on local VI to the case where the iteration is initialized with the value function of a stabilizing feedback policy, showing that the local update law preserves the stability of the associated control law. Then, we use the approximated cost function in a predictive controller framework and provide recursive feasibility, stability guarantees and an estimate of the region of attraction for a sufficiently long prediction horizon. The proposed approach is evaluated in simulation against a predictive controller which uses VI over the whole state space, and a predictive controller without terminal costs, to show the advantages of the proposed controller.

关键词： Costs Optimal control Europe Aerospace electronics Cost function Approximation error Stability analysis

Tube-based MPC for Two-Timescale Discrete-Time Nonlinear Processes with Robust control Contraction Metrics*

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Tube-based MPC for Two-Timescale Discrete-Time Nonlinear Pro...

IEEE Conference on Decision and control

作者： Yang Guo Philipp Sauerteig Stefan Streif Automatic Control and System Dynamics Laboratory Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Robust control Measurement Hands Computational modeling Crops Computational efficiency Numerical models Optimization Predictive control Farming

Soft sensor for monitoring dynamic changes in cell composition 9

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Soft sensor for monitoring dynamic changes in cell compositi...

9th IFAC Conference on Foundations of systems Biology in Engineering, FOSBE 2022

作者： Espinel-Ríos, Sebastián Morabito, Bruno Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

ISBN: (纸本)9781713867890

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia coli's metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens. Copyright © 2022 The Authors.

关键词： Metabolism

Approximate infinite-horizon predictive control

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Approximate infinite-horizon predictive control

IEEE Conference on Decision and control

作者： Lukas Beckenbach Stefan Streif Laboratory for Automatic Control and System Dynamics Technische Universitt Chemnitz Chemnitz Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Predictive control is frequently used for control problems involving constraints. Being an optimization based technique utilizing a user specified so-called stage cost, performance properties, i.e., bounds on the infinite horizon accumulated stage cost, aside closed-loop stability are of interest. To achieve good performance and to influence the region of attraction associated with the prediction horizon, the terminal cost of the predictive controller’s optimization objective is a key design factor. Approximate dynamic programming refers to one particular approximation paradigm that pursues iterative cost adaptation over a state domain. Troubled by approximation errors, the associated approximate optimal controller is, in general, not necessarily stabilizing nor is its performance quantifiable on the entire approximation domain. Using a parametric terminal cost trained via approximate dynamic programming, a stabilizing predictive controller is proposed whose performance can directly be related to cost approximation errors. The controller further ensures closed-loop asymptotic stability beyond the training domain of the approximate optimal controller associated to the terminal cost.

关键词： Training Asymptotic stability Costs Approximation error Cost function Stability analysis Dynamic programming

Experimental verification of an online traction parameter identification method

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arXiv 2023年

作者： Kobelski, Alexander Osinenko, Pavel Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Laboratory Germany Skolkovo Institute of Science and Technology Computational and Data Science and Engineering Center Moscow Russia

Traction parameters, that characterize the ground-wheel contact dynamics, are the central factor in the energy efficiency of vehicles. To optimize fuel consumption, reduce wear of tires, increase productivity etc., knowledge of current traction parameters is unavoidable. Unfortunately, these parameters are difficult to measure and require expensive force and torque sensors. An alternative way is to use system identification to determine them. In this work, we validate such a method in field experiments with a mobile robot. The method is based on an adaptive Kalman filter. We show how it estimates the traction parameters online, during the motion on the field, and compare them to their values determined via a 6-directional force-torque sensor installed for verification. Data of adhesion slip ratio curves is recorded and compared to curves from literature for additional validation of the method. The results can establish a foundation for a number of optimal traction methods. © 2023, CC BY-NC-ND.

关键词： Parameter estimation

Physics-informed neural networks for parameter learning of wildfire spreading

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arXiv 2024年

作者： Vogiatzoglou, Konstantinos Papadimitriou, C. Bontozoglou, V. Ampountolas, K. System Dynamics Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Transport Processes & Process Equipment Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop datadriven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

Optimal control and dynamic modulation of the ATPase gene expression for enforced ATP wasting in batch fermentations

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IFAC-PapersOnLine 2022年第7期55卷 174-180页

作者： Sebastián Espinel-Ríos Bruno Morabito Johannes Pohlodek Katja Bettenbrock Steffen Klamt Rolf Findeisen Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Competitive biotechnological processes need to operate over various conditions and adapt to changing economic contexts. Dynamic ATP turnover allows trading off declines in biomass formation and volumetric productivity for enhancements of product yields in fermentations where the product pathway is linked to ATP synthesis. To facilitate its practical implementation, we propose to dynamically manipulate the cellular ATP turnover by putting the ATPase enzyme, which hydrolyzes ATP into ADP, under the control of an optogenetic gene expression system. This allows achieving dynamic control of the ATP wasting online via a tunable external input. While light as control input is promising because it is easily tunable, generally non-invasive/non-toxic, and more affordable than classical chemical inducers, it makes the overall control task challenging. Thus, we derive an expanded version of dynamic enzyme-cost flux balance analysis that takes into account the dynamics of the optogenetic actuator. We then formulate a suitable optimal control problem to find optimal inputs for achieving the desired process performance. We test our approach in simulations using the batch anaerobic lactate fermentation of glucose by Escherichia coli as a case study.

关键词： ATP wasting ATPase optimal control gene expression optogenetics batch fermentation

Transient Stability Analysis of Power Grids with Admissible and Maximal Robust Positively Invariant Sets

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arXiv 2021年

作者： Aschenbruck, Tim Esterhuizen, Willem Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Laboratory Germany

The energy transition is causing many stability-related challenges for power systems. Transient stability refers to the ability of a power grid’s bus angles to retain synchronism after the occurrence of a major fault. In this paper a set-based approach is presented to assess the transient stability of power systems. The approach is based on the theory of barriers, to obtain an exact description of the boundaries of admissible sets and maximal robust positively invariant sets, respectively. We decompose a power system into generator and load components, replace couplings with bounded disturbances and obtain the sets for each component separately. From this we deduce transient stability properties for the entire system. We demonstrate the results of our approach through an example of one machine connected to one load and a multi-machine *** Codes 93A15, 93-08, 93D99 © 2021, CC BY.

关键词： Nonlinear analysis

Optimal control of centrifugal spreader

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arXiv 2021年

作者： Rußwurm, Franz Osinenko, Pavel Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Laboratory Germany

MSC Codes 37M05, 93B45, 93C15Achieving an evenly distributed fertilization spread pattern is a complex technical task. A corresponding control algorithm must account for the tractor movement, the settings of the spreader, the prescribed dosage as well as machine constraints. It dictates, in particular, the fertilization process needs be estimated ahead to achieve an optimized spread pattern. The presented work is concerned with the development of a predictive control scheme for optimized fertilizer application using modeling of the tractor moving on the field and the spread pattern in form of a crescent behind the tractor. In particular, the form of the spread pattern is modeled via four normal distributions, two for each side of the pattern. The control goal is to achieve a desired fertilizer distribution on the field. The study presents three algorithms for comparison: a one-step optimization and two approaches using model-predictive control - one with a simplified model of the spread pattern in the prediction horizon, and one with a comprehensive one model, respectively. The best results are obtained with model-predictive control using the comprehensive model. © 2021, CC BY-NC-ND.

关键词： Normal distribution