arXiv subjects

John Lygeros

Publications and source records attributed to John Lygeros.

At least 91 records · Page 5Linked to original sources

arXiv2024

Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing

In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to simulate the motion of virtual agents along the stress produced in a loaded part. Agent trajectories are then used as print trajectories. With this approach, the complex global trajectory generation problem is subdivided into a set of sequential and computationally efficient quadratic programs. Through comprehensive evaluations in both simulation and experiments, we compare our method with state-of-the-art approaches. Our results highlight a remarkable improvement in computational efficiency, achieving a 115x faster computation speed than existing methods. This efficiency, combined with the possibility to tune the trajectories spacing to match the deposition process constraints, makes the potential integration of our approach into existing 3D printing processes seamless. Additionally, the open-hole tensile specimen produced on a conventional fused filament fabrication set-up with our algorithm achieve a notable ~10% improvement in specific modulus compared to existing trajectory optimization methods.

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arXiv2024

A distributed framework for linear adaptive MPC

Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $\ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.

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arXiv2024

Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes

We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is implemented inexactly using an iterative solver for linear systems. In the classical dynamic programming literature, a similar principle is deployed in optimistic policy iteration, where an a-priori fixed-number of iterations of value iteration is used to inexactly solve the policy evaluation step. Inspired by the connection between policy iteration and semismooth Newton's method, we investigate a class of iPI methods that mimic the inexact variants of semismooth Newton's method by adopting a parametric stopping condition to regulate the level of inexactness of the policy evaluation step. For this class of methods we discuss local and global convergence properties and derive a practical range of values for the stopping-condition parameter that provide contraction guarantees. Our analysis is general and therefore encompasses a variety of iterative solvers for policy evaluation, including the standard value iteration as well as more sophisticated ones such as GMRES. As underlined by our analysis, the selection of the inner solver is of fundamental importance for the performance of the overall method. We therefore consider different iterative methods to solve the policy evaluation step and analyze their applicability and contraction properties when used for policy evaluation. We show that the contraction properties of these methods tend to be enhanced by the specific structure of policy evaluation and that there is margin for substantial improvement in terms of convergence rate. Finally, we study the numerical performance of different instances of inexact policy iteration on large-scale MDPs for the design of health policies to control the spread of infectious diseases in epidemiology.

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arXiv2024

A Coupled Optimization Framework for Correlated Equilibria in Normal-Form Game

In competitive multi-player interactions, simultaneous optimality is a key requirement for establishing strategic equilibria. This property is explicit when the game-theoretic equilibrium is the simultaneously optimal solution of coupled optimization problems. However, no such optimization problems exist for the correlated equilibrium, a strategic equilibrium where the players can correlate their actions. We address the lack of a coupled optimization framework for the correlated equilibrium by introducing an {unnormalized game} -- an extension of normal-form games in which the player strategies are lifted to unnormalized measures over the joint actions. We show that the set of fully mixed generalized Nash equilibria of this unnormalized game is a subset of the correlated equilibrium of the normal-form game. Furthermore, we introduce an entropy regularization to the unnormalized game and prove that the entropy-regularized generalized Nash equilibrium is a sub-optimal correlated equilibrium of the normal form game where the degree of sub-optimality depends on the magnitude of regularization. We prove that the entropy-regularized unnormalized game has a closed-form solution, and empirically verify its computational efficacy at approximating the correlated equilibrium of normal-form games.

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arXiv2024

BIG Hype: Best Intervention in Games via Distributed Hypergradient Descent

Hierarchical decision making problems, such as bilevel programs and Stackelberg games, are attracting increasing interest in both the engineering and machine learning communities. Yet, existing solution methods lack either convergence guarantees or computational efficiency, due to the absence of smoothness and convexity. In this work, we bridge this gap by designing a first-order hypergradient-based algorithm for Stackelberg games and mathematically establishing its convergence using tools from nonsmooth analysis. To evaluate the \textit{hypergradient}, namely, the gradient of the upper-level objective, we develop an online scheme that simultaneously computes the lower-level equilibrium and its Jacobian. Crucially, this scheme exploits and preserves the original hierarchical and distributed structure of the problem, which renders it scalable and privacy-preserving. We numerically verify the computational efficiency and scalability of our algorithm on a large-scale hierarchical demand-response model.

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arXiv2024

Data-Driven Extrusion Force Control Tuning for 3D Printing

The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods improve the accuracy and repeatability of 3D prints. However, optimal tuning of controllers for given process parameters and design geometry is often a challenge with manually tuned controllers resulting in inconsistent and suboptimal results. This work employs Bayesian optimization to identify the optimal controller parameters. Additionally, we explore transfer learning in the context of 3D printing by leveraging prior information from past trials. By integrating optimized extrusion force control and transfer learning, we provide a novel framework for closed-loop 3D printing and propose an automated calibration routine that produces high-quality prints for a desired combination of print settings, material, and shape.

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arXiv2024

Force Controlled Printing for Material Extrusion Additive Manufacturing

In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion. The amount of material to be extruded at each printing location is pre-computed by a planning software. This approach is inherently unable to adapt the extrusion to external and unexpected disturbances, and the quality of the results strongly depends on a number of modeling and tuning parameters. To overcome these limitations, we propose the first framework for Force Controlled Printing for material extrusion additive manufacturing. We utilize a custom-built extruder to measure the extrusion force in real time, and use this quantity as feedback to continuously control the material flow in closed-loop. We demonstrate the existence of a strong correlation between extrusion force and line width, which we exploit to deposit lines of desired width in a width range of 33 % up to 233 % of the nozzle diameter. We also show how Force Controlled Printing outperforms conventional feed-forward extrusion in print quality and disturbance rejection, while requiring little tuning and automatically adapting to changes in the hardware settings. With no adaptation, Force Controlled Printing can deposit lines of desired width under severe disturbances in bed leveling, such as at layer heights ranging between 20 % and 200 % of the nominal height.

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arXiv2024

Control Strategies for Recommendation Systems in Social Networks

A closed-loop control model to analyze the impact of recommendation systems on opinion dynamics within social networks is introduced. The core contribution is the development and formalization of model-free and model-based approaches to recommendation system design, integrating the dynamics of social interactions within networks via an extension of the Friedkin-Johnsen (FJ) model. Comparative analysis and numerical simulations demonstrate the effectiveness of the proposed control strategies in maximizing user engagement and their potential for influencing opinion formation processes.

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arXiv2024

Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data

It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identification of continuous-time stochastic parametric Wiener models through stochastic approximation. The method is applicable to generic model parameterizations and, as demonstrated in the numerical simulation examples, it is robust with respect to the assumptions on the spectrum of the disturbance process.

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arXiv2024

Online Feedback Equilibrium Seeking

This paper proposes a unifying design framework for dynamic feedback controllers that track solution trajectories of time-varying generalized equations, such as local minimizers of nonlinear programs or competitive equilibria (e.g., Nash) of non-cooperative games. Inspired by the feedback optimization paradigm, the core idea of the proposed approach is to re-purpose classic iterative algorithms for solving generalized equations (e.g., Josephy--Newton, forward-backward splitting) as dynamic feedback controllers by integrating online measurements of the continuous-time nonlinear plant. Sufficient conditions for closed-loop stability and robustness of the algorithm-plant cyber-physical interconnection are derived in a sampled-data setting by combining and tailoring results from (monotone) operator, fixed-point, and nonlinear systems theory. Numerical simulations on smart building automation and competitive supply-chain management are presented to support the theoretical findings.

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arXiv2023

Data-Driven Robust Reinforcement Learning Control of Uncertain Nonlinear Systems: Towards a Fully-Automated, Insulin-Based Artificial Pancreas

In this paper, a novel robust tracking control scheme for a general class of discrete-time nonlinear systems affected by unknown bounded uncertainty is presented. By solving a parameterized optimal tracking control problem subject to the unknown nominal system and a suitable cost function, the resulting optimal tracking control policy can ensure closed-loop stability by achieving a sufficiently small tracking error for the original uncertain nonlinear system. The computation of the optimal tracking controller is accomplished through the derivation of a novel Q-function-based $λ$-Policy Iteration algorithm. The proposed algorithm not only enjoys rigorous theoretical guarantees, but also avoids technical weaknesses of conventional reinforcement learning methods. By employing a data-driven, critic-only least squares implementation, the performance of the proposed algorithm is evaluated to the problem of fully-automated, insulin-based, closed-loop glucose control for patients diagnosed with Type 1 and Type 2 Diabetes Mellitus. The U.S. FDA-accepted DMMS.R simulator from the Epsilon Group is used to conduct a comprehensive in silico clinical campaign on a rich set of virtual subjects under completely unannounced meal and exercise settings. Simulation results underline the superior glycaemic behavior achieved by the derived approach, as well as its overall maturity for the design of highly-effective, closed-loop drug delivery systems for personalized medicine.

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arXiv2023

On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control

Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions. While the asymptotic stability of such algorithms is well studied, their finite-time performance has not received much attention. In this work, we quantify the performance of suboptimal linear model predictive control in terms of the additional closed-loop cost incurred due to performing only a finite number of optimization iterations. Leveraging this novel analysis framework, we propose a novel suboptimal MPC algorithm with a diminishing horizon length and finite-time closed-loop performance guarantees. This analysis allows the designer to plan a limited computational power budget distribution to achieve a desired performance level. We provide numerical examples to illustrate the algorithm's transient behavior and computational complexity.

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arXiv2023

Urban traffic congestion control: a DeePC change

Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers. By leveraging behavioral system theory and data-driven control, this paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights. To validate our approach, we consider a high-fidelity case study using the state-of-the-art simulation software package Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$_2$ emissions, demonstrating its potential for effective traffic management

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arXiv2023

Multi-Step Optimal Tracking Control of Unknown Nonzero-Sum Games based on Least Squares and Linear Programming: An Application to a Fully-Automated, Dual-Hormone Artificial Pancreas

We consider the problem of optimal tracking control of unknown discrete-time nonlinear nonzero-sum games. The related state-of-art literature is mostly focused on Policy Iteration algorithms and multiple neural network approximation, which may lead to practical implementation challenges and high computational burden. To overcome these problems, we propose a novel Q-function-based multi-step Value Iteration algorithm, which provides the potential to accelerate convergence speed and improve the quality of solutions, with an easy-to-realize initialization condition. A critic-only least squares implementation approach is then employed, which alleviates the computational complexity of commonly used multiple neural network-based methods. Afterwards, by introducing the coupled Bellman operator, a novel linear programming approach is derived, based on which Nash equilibria can be approximately computed by solving a set of tractable finite-dimensional optimization problems. We evaluate the tracking control capabilities of the proposed algorithms to the problem of fully-automated dual-hormone (i.e., insulin and glucagon) glucose control in Type 1 Diabetes Mellitus. The U.S. FDA-accepted DMMS.R simulator from the Epsilon Group is used to conduct extensive in-silico clinical studies on virtual patients under a variety of completely unannounced meal and exercise scenarios. Simulation results demonstrate the high reliability and exceptional performance of the proposed multi-step algorithmic framework to critical complex systems.

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arXiv2023

Data-driven optimal control via linear programming: boundedness guarantees

The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic programming setting. Despite its simple formulation, versatility, and predisposition to be employed in model-free settings, the LP approach has not enjoyed the same popularity as the other methods. The reason is the often poor scalability of the exact LP approach and the difficulty to obtain bounded solutions for a reasonable amount of constraints. We mitigate these issues here, by investigating fundamental geometric features of the LP and developing sufficient conditions to guarantee finite solutions with minimal constraints. In the model-free context, we show that boundedness can be guaranteed by a suitable choice of dataset and objective function.

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arXiv2023

Experimental Validation for Distributed Control of Energy Hubs

As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory. However, most of these strategies have been tested either only in simulated environments or small prosumer units as opposed to larger energy hubs. This simulation reality gap has hindered large-scale implementation and practical application of these method. In this paper, we aim to experimentally validate the performance of a novel multi-horizon distributed model predictive controller for an energy hub network by implementing the controller on a complete network of hubs comprising of a real energy hub inter-faced with multiple virtual hubs. The experiments are done using two different network topologies and the controller shows promising results in both setups.

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arXiv2023

Efficient safe learning for controller tuning with experimental validation

Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationally inefficient. In this paper, we propose a novel approach to safe learning by formulating a series of optimization problems instead of a grid search. We also develop a method for initializing the optimization problems to guarantee feasibility while using numerical solvers. The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision. Experimental validation on an industrial-grade precision motion system confirms that the proposed algorithm achieves 30% better tracking at sub-micrometer precision as a state-of-the-art safe learning algorithm, improves the default auto-tuning solution, and reduces the computational cost seven times compared to learning algorithms based on exhaustive search.

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arXiv2023

Data-driven representations of conical, convex, and affine behaviors

The paper studies conical, convex, and affine models in the framework of behavioral systems theory. We investigate basic properties of such behaviors and address the problem of constructing models from measured data. We prove that closed, shift-invariant, conical, convex, and affine models have the intersection property, thereby enabling the definition of most powerful unfalsified models based on infinite-horizon measurements. We then provide necessary and sufficient conditions for representing conical, convex, and affine finite-horizon behaviors using raw data matrices, expressing persistence of excitation requirements in terms of non-negative rank conditions. The applicability of our results is demonstrated by a numerical example arising in population ecology.

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