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2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Miao, Ailun Yang, Yi Kunming University of Science and Technology Faculty of Architecture and City Planning Kunming China

ISBN: (纸本)9798331529147

Modern and contemporary architectural heritage possesses unique architectural value, yet often suffers from poor thermal comfort and high energy consumption. Their preservation and reuse require a synergistic optimization of value preservation with other environmental, social, and economic performance aspects. The multi-objective optimization renovation decision-making system for modern heritage buildings, based on intelligent algorithms, distinguishes itself from previous experience-based approaches to building heritage reuse by conducting targeted quantitative analysis of the object to be renovated. This system clearly defines the goals and analyzes the benefits of renovation measures on those goals, ensuring that renovations are conducted within the framework of policies and protection regulations while anticipating their benefits. Incorporating multi-objective optimization algorithms into the multi-objective optimization renovation decision-making system for modern heritage building reuse holds significant importance. This approach adopts a 'from engineering to theory and back to engineering' methodology, where targeted analysis of the heritage building is conducted, followed by selection of appropriate renovation points and critical thermal parameters. Utilizing intelligent algorithms, optimization is performed to select parameters that meet the defined objectives, and specific renovation measures are determined based on these parameter selections. © 2024 IEEE.

关键词： optimization algorithms

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On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1715-1720页

作者： Chakrabarti, Kushal Chopra, Nikhil Tata Consultancy Serv Res Div Data & Decis Sci Mumbai 400607 India Univ Maryland Coll Pk Dept Mech Engn College Pk MD 20742 USA

This letter considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurements. The recently proposed Iteratively Preconditioned Gradient-Descent (IPG) observer, a Newton-type observer, has been empirically shown to have improved robustness against measurement noise than the prominent nonlinear observers, a property that other Newton-type observers lack. However, no theoretical guarantees on the convergence of the IPG observer were provided. This letter presents a rigorous convergence analysis of the IPG observer for a class of nonlinear systems in deterministic settings, proving its local linear convergence to the actual trajectory. The assumptions are standard in the existing literature of Newton-type observers, and the analysis further confirms the relation of IPG observer with Newton observer, which was only hypothesized earlier.

关键词： Estimation observers for nonlinear systems optimization algorithms

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Contraction Analysis of Continuation Method for Suboptimal Model Predictive Control

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 3261-3266页

作者： Shima, Ryotaro Ito, Yuji Miyano, Tatsuya Toyota Cent Res & Dev Labs Inc Green Fuel Res Domain Nagakute Aichi 4801192 Japan Toyota Cent Res & Dev Labs Inc Appl Math Res Domain Nagakute Aichi 4801192 Japan

This letter analyzes the contraction property of the nonlinear systems controlled by suboptimal model predictive control (MPC) using the continuation method. We propose a contraction metric that reflects the hierarchical dynamics inherent in the continuation method. We derive a pair of matrix inequalities that elucidate the impact of suboptimality on the contraction of the optimally controlled closed-loop system. A numerical example is presented to verify our contraction analysis. Our results are applicable to other MPCs than stabilization, including economic MPC.

关键词： Measurement Trajectory Linear matrix inequalities Vectors Predictive control Convergence Stability criteria optimization Numerical stability Mathematical models Predictive control for nonlinear systems optimization algorithms time-varying systems

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Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 2817-2822页

作者： van Wijk, David E. J. Coogan, Samuel Molnar, Tamas G. Majji, Manoranjan Hobbs, Kerianne L. Texas A&M Univ Coll Stn Dept Aerosp Engn College Stn TX 77845 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Wichita State Univ Dept Mech Engn Wichita KS 67260 USA US Air Force Auton Capabil Team Res Lab Wright Patterson AFB OH 45433 USA

Obtaining a controlled invariant set is crucial for safety-critical control with control barrier functions (CBFs) but is non-trivial for complex nonlinear systems and constraints. Backup control barrier functions allow such sets to be constructed online in a computationally tractable manner by examining the evolution (or flow) of the system under a known backup control law. However, for systems with unmodeled disturbances, this flow cannot be directly computed, making the current methods inadequate for assuring safety in these scenarios. To address this gap, we leverage bounds on the nominal and disturbed flow to compute a forward invariant set online by ensuring safety of an expanding norm ball tube centered around the nominal system evolution. We prove that this set results in robust control constraints which guarantee safety of the disturbed system via our Disturbance-Robust Backup Control Barrier Function (DR-bCBF) solution. The efficacy of the proposed framework is demonstrated in simulation, applied to a double integrator problem and a rigid body spacecraft rotation problem with rate constraints.

关键词： Safety Uncertainty Trajectory optimization Nonlinear systems Aerodynamics Space vehicles Jacobian matrices Closed loop systems Vectors Constrained control Lyapunov methods optimization algorithms

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Feedback-Based optimization With Sub-Weibull Gradient Errors and Intermittent Updates

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 2521-2526页

作者： Ospina, Ana M. Bastianello, Nicola Dall'Anese, Emiliano Univ Colorado Dept Elect Comp & Energy Engn Boulder CO 80309 USA Univ Padua Dept Informat Engn I-35131 Padua Italy

This letter considers a feedback-based projected gradient method for optimizing systems modeled as algebraic maps. The focus is on a setup where the gradient is corrupted by random errors that follow a sub-Weibull distribution, and where the measurements of the output - which replace the input-output map of the system in the algorithmic updates - may not be available at each iteration. The sub-Weibull error model is particularly well-suited in frameworks where the cost of the problem is learned via Gaussian Process (GP) regression (from functional evaluations) concurrently with the execution of the algorithm;however, it also naturally models setups where nonparametric methods and neural networks are utilized to estimate the cost. Using the sub-Weibull model, and with Bernoulli random variables modeling missing measurements of the system output, we show that the online algorithm generates points that are within a bounded error from the optimal solutions. In particular, we provide error bounds in expectation and in high probability. Numerical results are presented in the context of a demand response problem in smart power grids.

关键词： Costs Noise measurement Random variables Measurement uncertainty Convergence Computational modeling Particle measurements optimization optimization algorithms

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Distributed Stochastic Dual Subgradient for Constraint-Coupled optimization

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 644-649页

作者： Camisa, Andrea Notarnicola, Ivano Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy

In this letter we consider a distributed stochastic optimization framework in which agents in a network aim to cooperatively learn an optimal network-wide policy. The goal is to compute local functions to minimize the expected value of a given cost, subject to individual constraints and average coupling constraints. In order to handle the challenges of the distributed stochastic context, we resort to a Lagrangian duality approach that allows us to derive an associated stochastic dual problem with a separable structure. Thus, we propose a distributed algorithm, without a central coordinator, that exploits consensus iterations and stochastic approximation to find an optimal solution to the problem, with attractive scalability properties. We demonstrate convergence of the proposed scheme and validate its behavior through simulations.

关键词： optimization Couplings Stochastic processes Resource management Distributed algorithms Minimization IEEE members optimization algorithms large-scale systems distributed control stochastic optimization

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A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

arXiv

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arXiv 2022年

作者： Dagréou, Mathieu Ablin, Pierre Vaiter, Samuel Moreau, Thomas Inria CEA Université Paris-Saclay Palaiseau France CNRS Université Côte d’Azur LJAD Nice France CNRS Université Paris-Dauphine PSL-University Paris France

Bilevel optimization, the problem of minimizing a value function which involves the arg-minimum of another function, appears in many areas of machine learning. In a large scale empirical risk minimization setting where the number of samples is huge, it is crucial to develop stochastic methods, which only use a few samples at a time to progress. However, computing the gradient of the value function involves solving a linear system, which makes it difficult to derive unbiased stochastic estimates. To overcome this problem we introduce a novel framework, in which the solution of the inner problem, the solution of the linear system, and the main variable evolve at the same time. These directions are written as a sum, making it straightforward to derive unbiased estimates. The simplicity of our approach allows us to develop global variance reduction algorithms, where the dynamic of all variables is subject to variance reduction. We demonstrate that SABA, an adaptation of the celebrated SAGA algorithm in our framework, has O(T1 ) convergence rate, and that it achieves linear convergence under Polyak-Lojasciewicz assumption. This is the first stochastic algorithm for bilevel optimization that verifies either of these properties. Numerical experiments validate the usefulness of our method. © 2022, CC BY.

关键词： optimization algorithms

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Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 2853-2858页

作者： Gokhale, Anand Davydov, Alexander Bullo, Francesco UC Santa Barbara Ctr Control Dynam Syst & Computat Santa Barbara CA 93106 USA

In this letter we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsmooth convex and a smooth component. First, we show that the cost function decreases monotonically along the trajectories of the proximal gradient dynamics. We then introduce a new condition that guarantees exponential convergence of the cost function to its optimal value, and show that this condition implies the proximal Polyak-& Lstrok;ojasiewicz condition. We also show that the proximal Polyak-& Lstrok;ojasiewicz condition guarantees exponential convergence of the cost function. Moreover, we extend these results to time-varying optimization problems, providing bounds for equilibrium tracking. Finally, we discuss applications of these findings, including the LASSO problem, certain matrix based problems and a numerical experiment on a feed-forward neural network.

关键词： Convergence Cost function Heuristic algorithms Trajectory Machine learning algorithms Costs Prediction algorithms Neuroscience Machine learning Encoding optimization algorithms nonconvex optimization nonlinear systems

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The Whale optimization Algorithm in Abrasive Water Jet Machining of Tool Steel 27

The Whale Optimization Algorithm in Abrasive Water Jet Machi...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Kawecka, Elzbieta The Jacob of Paradies University Faculty of Technology Teatralna 25 Gorzów Wielkopolski66-400 Poland

The paper presents possibility of Whale optimization Algorithm application into abrasive waterjet (AWJ) machining of tool steel. Based on the control parameters of the process of cutting tool steel with AWJ, the objective function was determined using the Response Surface Methodology (RSM). Then the process of optimization and established the set of control parameters which the value of the objective function reaches the extremum value was carried out. Also, the effectiveness of this algorithm for optimizing the parameters of the studied process was determined. The calculated on the basis WOA value of optimum was comparison with value of the best effect determined experimentally. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Abrasive Water Jet (AWJ) AWJ cutting process meta-heuristic algorithms optimization algorithms Whale optimization Algorithm (WOA)

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Iterative Learning-Based Trajectory optimization Using Fourier Series Basis Functions

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 2180-2185页

作者： Drallmeier, Joseph A. Siegel, Jason B. Stefanopoulou, Anna G. Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

This letter presents an iterative, online trajectory optimization algorithm for systems performing repetitive processes. While typical iterative learning techniques are formulated for tracking control applications, a precise definition of the tracking reference is required. In repetitive applications where the optimal tracking reference is not fully defined, there exists an opportunity to improve system performance by altering the trajectory of the system based on information rich signals from previous cycles. In this letter, we develop an algorithm to optimize the parameterized trajectory of a system in real time utilizing constrained optimization of a cost function generated from the performance values of the previous cycle. Simulation results are used to illustrate the implementation of this iterative trajectory optimization framework while also benchmarking the performance against a norm optimal iterative learning controller with perfect system knowledge.

关键词： Cost function Trajectory optimization Indium tin oxide Uncertainty Manipulator dynamics Iterative algorithms Indexes optimization iterative learning control optimization algorithms

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