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Book ; Online: Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control

Shi, Shengling / Tsiamis, Anastasios / De Schutter, Bart

2023

Abstract: For a receding-horizon controller with a known system and with an approximate terminal value function, it is well-known that increasing the prediction horizon can improve its control performance. However, when the prediction model is inexact, a larger ... ...

Abstract	For a receding-horizon controller with a known system and with an approximate terminal value function, it is well-known that increasing the prediction horizon can improve its control performance. However, when the prediction model is inexact, a larger prediction horizon also causes propagation and accumulation of the prediction error. In this work, we aim to analyze the effect of the above trade-off between the modeling error, the terminal value function error, and the prediction horizon on the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a performance upper bound is obtained and suggests that for many cases, the prediction horizon should be either 1 or infinity to improve the control performance, depending on the relative difference between the modeling error and the terminal value function error. The obtained suboptimality performance bound is also applied to provide end-to-end performance guarantees, e.g., regret bounds, for nominal receding-horizon LQ controllers in a learning-based setting.
Keywords	Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning
Subject code	629
Publishing date	2023-01-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Commercial Off-the-Shelf Components (COTS) in Realizing Miniature Implantable Wireless Medical Devices: A Review.

Khan, Sadeque Reza / Mugisha, Andrew J / Tsiamis, Andreas / Mitra, Srinjoy

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 10

Abstract: Over the past decade, there has been exponential growth in the per capita rate of medical patients around the world, and this is significantly straining the resources of healthcare institutes. Therefore, the reliance on smart commercial off-the-shelf ( ... ...

Abstract	Over the past decade, there has been exponential growth in the per capita rate of medical patients around the world, and this is significantly straining the resources of healthcare institutes. Therefore, the reliance on smart commercial off-the-shelf (COTS) implantable wireless medical devices (IWMDs) is increasing among healthcare institutions to provide routine medical services, such as monitoring patients' physiological signals and the remote delivery of therapeutic drugs. These smart COTS IWMDs reduce the necessity of recurring visits of patients to healthcare institutions and also mitigate physical contact, which can minimize the possibility of any potential spread of contagious diseases. Furthermore, the devices provide patients with the benefit of recuperating in familiar surroundings. As such, low-cost, ubiquitous COTS IWMDs have engendered the proliferation of telemedicine in healthcare to provide routine medical services. In this paper, a review work on COTS IWMDs is presented at a macro level to discuss the history of IWMDs, different networked COTS IWMDs, health and safety regulations of COTS IWMDs and the importance of organized procurement. Furthermore, we discuss the basic building blocks of IWMDs and how COTS components can contribute to build these blocks over widely researched custom-built application-specific integrated circuits.
MeSH term(s)	Humans ; Monitoring, Physiologic ; Prostheses and Implants ; Telemedicine
Language	English
Publishing date	2022-05-10
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22103635
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Linear Systems can be Hard to Learn

Tsiamis, Anastasios / Pappas, George J.

2021

Abstract: In this paper, we investigate when system identification is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension. Most prior ... ...

Abstract	In this paper, we investigate when system identification is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension. Most prior research in the finite sample regime falls in this category, focusing on systems that are directly excited by process noise. Statistically hard to learn linear system classes have worst-case sample complexity that is at least exponential with the system dimension, regardless of the identification algorithm. Using tools from minimax theory, we show that classes of linear systems can be hard to learn. Such classes include, for example, under-actuated or under-excited systems with weak coupling among the states. Having classified some systems as easy or hard to learn, a natural question arises as to what system properties fundamentally affect the hardness of system identifiability. Towards this direction, we characterize how the controllability index of linear systems affects the sample complexity of identification. More specifically, we show that the sample complexity of robustly controllable linear systems is upper bounded by an exponential function of the controllability index. This implies that identification is easy for classes of linear systems with small controllability index and potentially hard if the controllability index is large. Our analysis is based on recent statistical tools for finite sample analysis of system identification as well as a novel lower bound that relates controllability index with the least singular value of the controllability Gramian. Comment: Under review
Keywords	Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning ; Mathematics - Optimization and Control ; Statistics - Machine Learning
Publishing date	2021-04-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: State-Output Risk-Constrained Quadratic Control of Partially Observed Linear Systems

Koumpis, Nikolas / Tsiamis, Anastasios / Kalogerias, Dionysios

2022

Abstract: We propose a methodology for performing risk-averse quadratic regulation of partially observed Linear Time-Invariant (LTI) systems disturbed by process and output noise. To compensate against the induced variability due to both types of noises, state ... ...

Abstract	We propose a methodology for performing risk-averse quadratic regulation of partially observed Linear Time-Invariant (LTI) systems disturbed by process and output noise. To compensate against the induced variability due to both types of noises, state regulation is subject to two risk constraints. The latter renders the resulting controller cautious of stochastic disturbances, by restricting the statistical variability, namely, a simplified version of the cumulative expected predictive variance of both the state and the output. Our proposed formulation results in an optimal risk-averse policy that preserves favorable characteristics of the classical Linear Quadratic (LQ) control. In particular, the optimal policy has an affine structure with respect to the minimum mean square error (mmse) estimates. The linear component of the policy regulates the state more strictly in riskier directions, where the process and output noise covariance, cross-covariance, and the corresponding penalties are simultaneously large. This is achieved by "inflating" the state penalty in a systematic way. The additional affine terms force the state against pure and cross third-order statistics of the process and output disturbances. Another favorable characteristic of our optimal policy is that it can be pre-computed off-line, thus, avoiding limitations of prior work. Stability analysis shows that the derived controller is always internally stable regardless of parameter tuning. The functionality of the proposed risk-averse policy is illustrated through a working example via extensive numerical simulations.
Keywords	Mathematics - Optimization and Control ; Electrical Engineering and Systems Science - Systems and Control
Subject code	519
Publishing date	2022-04-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: How are policy gradient methods affected by the limits of control?

Ziemann, Ingvar / Tsiamis, Anastasios / Sandberg, Henrik / Matni, Nikolai

2022

Abstract: We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inevitably lead to noisy gradient estimates. We also give an example of a ... ...

Abstract	We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inevitably lead to noisy gradient estimates. We also give an example of a class of stable systems in which policy gradient methods suffer from the curse of dimensionality. Our results apply to both state feedback and partially observed systems.
Keywords	Mathematics - Optimization and Control ; Computer Science - Machine Learning
Publishing date	2022-06-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Design and Fabrication of a Fully-Integrated, Miniaturised Fluidic System for the Analysis of Enzyme Kinetics.

Tsiamis, Andreas / Buchoux, Anthony / Mahon, Stephen T / Walton, Anthony J / Smith, Stewart / Clarke, David J / Stokes, Adam A

Micromachines

2023 Volume 14, Issue 3

Abstract: The lab-on-a-chip concept, enabled by microfluidic technology, promises the integration of multiple discrete laboratory techniques into a miniaturised system. Research into microfluidics has generally focused on the development of individual elements of ... ...

Abstract	The lab-on-a-chip concept, enabled by microfluidic technology, promises the integration of multiple discrete laboratory techniques into a miniaturised system. Research into microfluidics has generally focused on the development of individual elements of the total system (often with relatively limited functionality), without full consideration for integration into a complete fully optimised and miniaturised system. Typically, the operation of many of the reported lab-on-a-chip devices is dependent on the support of a laboratory framework. In this paper, a demonstrator platform for routine laboratory analysis is designed and built, which fully integrates a number of technologies into a single device with multiple domains such as fluidics, electronics, pneumatics, hydraulics, and photonics. This facilitates the delivery of breakthroughs in research, by incorporating all physical requirements into a single device. To highlight this proposed approach, this demonstrator microsystem acts as a fully integrated biochemical assay reaction system. The resulting design determines enzyme kinetics in an automated process and combines reservoirs, three-dimensional fluidic channels, optical sensing, and electronics in a low-cost, low-power and portable package.
Language	English
Publishing date	2023-02-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2620864-7
ISSN	2072-666X
ISSN	2072-666X
DOI	10.3390/mi14030537
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Article: Adsorption Behavior of Asphaltenes and Resins on Kaolinite

Tsiamis, Aristeidis / Taylor Spencer E

Energy & Fuels. 2017 Oct. 19, v. 31, no. 10

2017

Abstract: Recent studies have shown that n-C7-precipitated asphaltenes adsorb onto nanoparticles to produce isotherms that are significantly influenced by the dispersed states of both the adsorbate and the adsorbent. In the present work, we investigate this ... ...

Abstract	Recent studies have shown that n-C7-precipitated asphaltenes adsorb onto nanoparticles to produce isotherms that are significantly influenced by the dispersed states of both the adsorbate and the adsorbent. In the present work, we investigate this behavior further by determining the adsorption of asphaltene and resin fractions isolated from four different sources onto kaolinite using the depletion method in toluene. Treated conventionally (amount adsorbed, Î, versus equilibrium bulk concentration, câ), adsorption isotherms for fixed initial concentrations (câ) of C5 and C7 asphaltenes and variable kaolinite mass (mâ) are found to be Type I as classified by IUPAC, whereas under the same experimental conditions C5âC7 resins exhibit Type III behavior. By fixing mâ and varying câ, however, Type II isotherms are produced by the resins. All of the adsorption results for the same fraction type were found to be very similar, irrespective of the source. The Types I and III isotherms are described very well by the thermodynamic solidâliquid equilibrium (SLE) model of Montoya et al. (Energy Fuels 2014, 28, 4963â4975) based on the association theory of Talu and Meunier (AIChE J. 1996, 42, 809â819). Individual isotherms (Î versus câ) are well-fitted by a shifted Langmuir equation for asphaltenes and by a general Freundlich (power law) relationship for resins. The SLE results verify that in toluene solution the adsorption behavior is complicated by concentration-dependent nanoaggregation of asphaltene species, whereas resinâresin interactions are weaker, but accompanied by adsorbent particle aggregation. On the other hand, when the adsorption data for each fraction type is replotted in terms of the ratio of the experimental parameter câ/mâ, as originally done by Wang et al. (Colloids Surfaces A: Physicochem. Eng. Aspects 2016, 504, 280â286), each set of data merges to a single isotherm which is reasonably well-approximated by a Langmuir-type relationship (we term this a âpseudo-Langmuir equationâ), which allows the maximum adsorption to be determined for the different adsorbate/adsorbent systems. The average maximum adsorbed amounts calculated in this way for each of the component types are very similar, being slightly larger for C7A compared with C5A, with the values for the C5âC7R fractions being generally lower and more variable, possibly reflecting some source dependence.
Keywords	adsorbents ; adsorption ; equations ; fuels ; kaolinite ; models ; nanoparticles ; resins ; sorption isotherms ; thermodynamics ; toluene
Language	English
Dates of publication	2017-1019
Size	p. 10576-10587.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	1483539-3
ISSN	1520-5029 ; 0887-0624
ISSN (online)	1520-5029
ISSN	0887-0624
DOI	10.1021%2Facs.energyfuels.7b01695
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Statistical Learning Theory for Control

Tsiamis, Anastasios / Ziemann, Ingvar / Matni, Nikolai / Pappas, George J.

A Finite Sample Perspective

2022

Abstract: This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been substantial progress across all areas of control, the theory is most well- ... ...

Abstract	This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been substantial progress across all areas of control, the theory is most well-developed when it comes to linear system identification and learning for the linear quadratic regulator, which are the focus of this manuscript. From a theoretical perspective, much of the labor underlying these advances has been in adapting tools from modern high-dimensional statistics and learning theory. While highly relevant to control theorists interested in integrating tools from machine learning, the foundational material has not always been easily accessible. To remedy this, we provide a self-contained presentation of the relevant material, outlining all the key ideas and the technical machinery that underpin recent results. We also present a number of open problems and future directions. Comment: Survey Paper, Submitted to Control Systems Magazine. Second version contains additional motivation for finite sample statistics and more detailed comparison with classical literature
Keywords	Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning ; Mathematics - Optimization and Control ; Statistics - Machine Learning
Subject code	370
Publishing date	2022-09-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Encrypted Distributed Lasso for Sparse Data Predictive Control

Alexandru, Andreea B. / Tsiamis, Anastasios / Pappas, George J.

2021

Abstract: The least squares problem with L1-regularized regressors, called Lasso, is a widely used approach in optimization problems where sparsity of the regressors is desired. This formulation is fundamental for many applications in signal processing, machine ... ...

Abstract	The least squares problem with L1-regularized regressors, called Lasso, is a widely used approach in optimization problems where sparsity of the regressors is desired. This formulation is fundamental for many applications in signal processing, machine learning and control. As a motivating problem, we investigate a sparse data predictive control problem, run at a cloud service to control a system with unknown model, using L1-regularization to limit the behavior complexity. The input-output data collected for the system is privacy-sensitive, hence, we design a privacy-preserving solution using homomorphically encrypted data. The main challenges are the non-smoothness of the L1-norm, which is difficult to evaluate on encrypted data, as well as the iterative nature of the Lasso problem. We use a distributed ADMM formulation that enables us to exchange substantial local computation for little communication between multiple servers. We first give an encrypted multi-party protocol for solving the distributed Lasso problem, by approximating the non-smooth part with a Chebyshev polynomial, evaluating it on encrypted data, and using a more cost effective distributed bootstrapping operation. For the example of data predictive control, we prefer a non-homogeneous splitting of the data for better convergence. We give an encrypted multi-party protocol for this non-homogeneous splitting of the Lasso problem to a non-homogeneous set of servers: one powerful server and a few less powerful devices, added for security reasons. Finally, we provide numerical results for our proposed solutions.
Keywords	Mathematics - Optimization and Control ; Computer Science - Cryptography and Security ; Electrical Engineering and Systems Science - Systems and Control
Publishing date	2021-04-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Adaptive Stochastic MPC under Unknown Noise Distribution

Stamouli, Charis / Tsiamis, Anastasios / Morari, Manfred / Pappas, George J.

2022

Abstract: In this paper, we address the stochastic MPC (SMPC) problem for linear systems, subject to chance state constraints and hard input constraints, under unknown noise distribution. First, we reformulate the chance state constraints as deterministic ... ...

Abstract	In this paper, we address the stochastic MPC (SMPC) problem for linear systems, subject to chance state constraints and hard input constraints, under unknown noise distribution. First, we reformulate the chance state constraints as deterministic constraints depending only on explicit noise statistics. Based on these reformulated constraints, we design a distributionally robust and robustly stable benchmark SMPC algorithm for the ideal setting of known noise statistics. Then, we employ this benchmark controller to derive a novel robustly stable adaptive SMPC scheme that learns the necessary noise statistics online, while guaranteeing time-uniform satisfaction of the unknown reformulated state constraints with high probability. The latter is achieved through the use of confidence intervals which rely on the empirical noise statistics and are valid uniformly over time. Moreover, control performance is improved over time as more noise samples are gathered and better estimates of the noise statistics are obtained, given the online adaptation of the estimated reformulated constraints. Additionally, in tracking problems with multiple successive targets our approach leads to an online-enlarged domain of attraction compared to robust tube-based MPC. A numerical simulation of a DC-DC converter is used to demonstrate the effectiveness of the developed methodology. Comment: To appear in L4DC 2022
Keywords	Electrical Engineering and Systems Science - Systems and Control ; Mathematics - Optimization and Control ; Statistics - Machine Learning
Subject code	006
Publishing date	2022-04-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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