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Autonomous navigation with convergence guarantees in complex dynamic environments☆

AUTOMATICA 2025年 173卷

作者： Dahlin, Albin Karayiannidis, Yiannis Chalmers Univ Technol Dept Elect Engn SE-41296 Gothenburg Sweden Lund Univ LTH Dept Automat Control SE-22100 Lund Sweden

This article addresses the obstacle avoidance problem for setpoint stabilization tasks in complex dynamic 2-D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed that integrates the favorable convergence characteristics of closed-form motion planning techniques with the intuitive representation of system constraints through Model Predictive control (MPC). The method is analytically proven to accomplish collision avoidance and convergence under soft conditions. Simulation scenarios using a non-holonomic unicycle robot is provided to showcase the efficacy of the control scheme. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Guidance navigation and control Autonomous systems modeling for control optimization control of constrained systems

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SPOCK: A proximal method for multistage risk-averse optimal control problems 22

SPOCK: A proximal method for multistage risk-averse optimal ...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Bodard, Alexander Moran, Ruairi Schuurmans, Mathijs Patrinos, Panagiotis Sopasakis, Pantelis Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland Katholieke Univ Leuven Dept Elect Engn Kasteelpk Arenberg 10 B-3001 Leuven Belgium

ISBN: (纸本)9781713872344

Risk-averse optimal control problems have gained a lot of attention in the last decade, mostly due to their attractive mathematical properties and practical importance. They can be seen as an interpolation between stochastic and robust optimal control approaches, allowing the designer to trade-off performance for robustness and vice-versa. Due to their stochastic nature, risk-averse problems are of a very large scale, involving millions of decision variables, which poses a challenge in terms of efficient computation. In this work, we propose a splitting for general risk-averse problems involving linear dynamical systems and show how to efficiently compute iterates on a GPU-enabled hardware. Moreover, we propose Spock - a new algorithm that utilizes the proposed splitting and takes advantage of the SuperMann scheme combined with fast directions from Anderson's acceleration method for enhanced convergence speed. We implement Spock in Julia as an open-source solver, which is amenable to warm-starting and massive parallelization. Copyright (c) 2023 The Authors.

关键词： Stochastic optimal control problems large-scale optimization problems modeling for control optimization risk-averse optimal control.

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Dynamic analysis and performance assessment of the Inertial Sea Wave Energy Converter (ISWEC) device via harmonic balance 14

Dynamic analysis and performance assessment of the Inertial ...

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14th IFAC Conference on control Applications in Marine Systems, Robotics, and Vehicles (CAMS)

作者： Carapellese, Fabio Pasta, Edoardo Faedo, Nicolas Giorgi, Giuseppe Politecn Torino Dept Mech & Aerosp Engn Marine Offshore Renewable Energy Lab I-10129 Turin Italy

Given the particular energy-maximising performance objective, wave energy converter (WEC) systems are prone to exhibit highly nonlinear behaviour. We present, in this paper, a detailed dynamic analysis and control synthesis for the Inertial Sea Wave Energy Converter (ISWEC) system, deriving and considering a comprehensive associated nonlinear model. In particular, we adopt a harmonic balance (HB) method to achieve this objective, producing the so-called amplitude-frequency curves (AFC) for the corresponding ISWEC nonlinear model, derived via a Lagrangian approach. We demonstrate that the system can present a variety of different behaviours which are completely neglected by its linear model counterpart. Leveraging both the efficiency, and convenient representation of the HB method, we synthesise so-called `passive' (i.e. proportional) energy-maximising controllers using a variety of input conditions. We provide a comparison of the obtained control parameters with those arising from standard linear modelling, showing a consistent improvement in performance by effectively considering the relevant nonlinear ISWEC dynamics. Copyright (C) 2022 The Authors.

关键词： modeling Wave energy converters Application of nonlinear analysis and design modeling for control optimization Energy systems

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Data-Driven Prediction and Predictive control Methods for Eco-Driving in Production Vehicles 2nd

Data-Driven Prediction and Predictive Control Methods for Ec...

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2nd modeling, Estimation and control Conference (MECC)

作者： Baby, Tinu Vellamattathil Sotoudeh, Seyedeh Mahsa HomChaudhuri, Baisravan Illinois Inst Technol Chicago IL 60616 USA

This paper presents a study of perception and robust model-predictive control (MPC) strategies in realistic traffic environments, which are simulated using data from real-world driving experiments. In this paper, we consider a heterogeneous traffic environment, which includes human-driven vehicles, and study the performance of currently available automation in production vehicles. We then present a data-driven preceding vehicle's velocity and position prediction algorithm, and a robust MPC strategy that optimizes fuel consumption and takes into account the prediction errors. Data used in this paper are taken from experiments using a 2018 Cadillac CT6 vehicle. Simulation results show up to 6.39% energy efficiency improvement. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Autonomous Vehicles Motion control Optimal control theory modeling for control optimization Learning and adaptation in autonomous vehicles.

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Approximating the model of a water distribution network as a Markov decision process 10

Approximating the model of a water distribution network as a...

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10th Vienna International Conference on Mathematical Modelling (MATHMOD)

作者： Misra, Rahul Wisniewski, Rafal Kallesoe, Carsten S. Aalborg Univ Dept Elect Syst Automat & Control Sect Aalborg Denmark Grundfos AS Poul Due Tensens Vej Bjerringbro Denmark

In this paper, our objective is to convert the model of a water distribution network described via Stochastic differential equations (SDE) into a Markov decision process (MDP). The motivation behind this work is that while MDP's represents the underlying dynamics for dynamic programming and reinforcement learning, the actual underlying model is best described via differential equations, and therefore, we would like to convert the SDE to MDP. We have applied Kushner's Markov chain approximation (MCA) method and verified it using a novel modified Monte Carlo method which can be considered as an alternative to the well-known Kushner's MCA. Both the methods approximate the value function and simulation studies show that the obtained value functions from both the methods converge to almost the same value. Copyright (C) 2022 The Authors.

关键词： modeling for control optimization Markov decision process Stochastic differential equations Dynamic programming Markov chain approximation Monte Carlo methods

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The time-freezing reformulation for numerical optimal control of complementarity Lagrangian systems with state jumps

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AUTOMATICA 2023年 158卷

作者： Nurkanovic, Armin Albrecht, Sebastian Brogliato, Bernard Diehl, Moritz Univ Freiburg Dept Microsyst Engn IMTEK Freiburg Germany Univ Freiburg Dept Math Freiburg Germany Siemens Technol Munich Germany Univ Grenoble Alpes INRIA CNRS Grenoble INPLJK Grenoble France

This paper introduces a novel time-freezing reformulation and numerical methods for optimal control of complementarity Lagrangian systems (CLS) with state jumps. We cover the difficult case when the system evolves on the boundary of the dynamic's feasible set after the state jump. In nonsmooth mechanics, this corresponds to inelastic impacts. The main idea of the time-freezing reformulation is to introduce a clock state and an auxiliary dynamical system whose trajectory endpoints satisfy the state jump law. When the auxiliary system is active, the clock state is not evolving, hence by taking only the parts of the trajectory when the clock state was active, we can recover the original solution. The resulting time-freezing system is a Filippov system that has jump discontinuities only in the first time derivative instead of the trajectory itself. This enables one to use the recently proposed Finite Elements with Switch Detection (Nurkanovic et al., 2022), which makes high accuracy numerical optimal control of CLS with impacts and friction possible. We detail how to recover the solution of the original system and show how to select appropriate auxiliary dynamics. The theoretical findings are illustrated on a nontrivial numerical optimal control example of a hopping one-legged robot. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Nonsmooth and discontinuous problems modeling for control optimization Numerical algorithms Algorithms and software

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On the universal transformation of data-driven models to control systems

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AUTOMATICA 2023年 149卷

作者： Peitz, Sebastian Bieker, Katharina Paderborn Univ Dept Comp Sci Paderborn Germany Paderborn Univ Dept Math Paderborn Germany

The advances in data science and machine learning have resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate predictions of complex systems such as the weather, disease models or the stock market. Predictive methods are often advertised to be useful for control, but the specifics are frequently left unanswered due to the higher system complexity, the requirement of larger data sets and an increased modeling effort. In other words, surrogate modeling for autonomous systems is much easier than for control systems. In this paper we present the framework QuaSiModO (QuantizationSimulation-modeling-optimization) to transform arbitrary predictive models into control systems and thus render the tremendous advances in data-driven surrogate modeling accessible for control. Our main contribution is that we trade control efficiency by autonomizing the dynamics - which yields mixed-integer control problems - to gain access to arbitrary, ready-to-use autonomous surrogate modeling techniques. We then recover the complexity of the original problem by leveraging recent results from mixed-integer optimization. The advantages of QuaSiModO are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model in use, and little prior knowledge requirements in control theory to solve complex control problems.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： modeling for control optimization Tracking Data -based control Switching controllers Linear nonlinear models

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SPOCK: A proximal method for multistage risk-averse optimal control problems

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IFAC-PapersOnLine 2023年第2期56卷 1944-1951页

作者： Alexander Bodard Ruairi Moran Mathijs Schuurmans Panagiotis Patrinos Pantelis Sopasakis Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BT9 5AH Northern Ireland UK KU Leuven Department of Electrical Engineering Kasteelpark Arenberg 10 3001 Leuven Belgium

Risk-averse optimal control problems have gained a lot of attention in the last decade, mostly due to their attractive mathematical properties and practical importance. They can be seen as an interpolation between stochastic and robust optimal control approaches, allowing the designer to trade-off performance for robustness and vice-versa. Due to their stochastic nature, risk-averse problems are of a very large scale, involving millions of decision variables, which poses a challenge in terms of efficient computation. In this work, we propose a splitting for general risk-averse problems involving linear dynamical systems and show how to efficiently compute iterates on a GPU-enabled hardware. Moreover, we propose Spock — a new algorithm that utilizes the proposed splitting and takes advantage of the SuperMann scheme combined with fast directions from Anderson's acceleration method for enhanced convergence speed. We implement Spock in Julia as an open-source solver, which is amenable to warm-starting and massive parallelization.

关键词： Stochastic optimal control problems large-scale optimization problems modeling for control optimization risk-averse optimal control

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Learning Models of Model Predictive controllers using Gradient Data 19

Learning Models of Model Predictive Controllers using Gradie...

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19th IFAC Symposium on System Identification (SYSID)

作者： Winqvist, Rebecka Venkitaraman, Arun Wahlberg, Bo KTH Royal Inst Technol EECS Div Decis & Control Syst Stockholm Sweden

This paper investigates the problem of controller identification given the data from a linear quadratic Model Predictive controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses the gradient information in the training process. This is motivated by the observation that recent differentiable convex optimization MPC solvers can provide both the optimal feedback law from the state to control input as well as the corresponding gradient. As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension. controller identification can here be used to find an approximate low complexity functional approximation of the controller. The eMPC is modelled using a Neural Network (NN) with Rectified Linear Units (ReLUs), since such NNs can represent any piece-wise affine function. A key motivation is to replace on-line solvers with neural networks to implement MPC and to simplify the evaluation of the function in larger input dimensions. We also study experimental design and model evaluation in this framework, and propose a hit-and-run sampling algorithm for input design. The proposed algorithms are illustrated and numerically evaluated on a second order MPC problem. Copyright (C) 2021 The Authors.

关键词： Identification for control data-driven control neural networks relevant to control and identification input and excitation design model predictive control modeling for control optimization

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Technical Devices Degradation Self-Analysis for Self-Maintenance Strategy: Crane Case Study 17th

Technical Devices Degradation Self-Analysis for Self-Mainten...

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17th IFAC Symposium on Information control Problems in Manufacturing (INCOM)

作者： Szpytko, Janusz Duarte, Yorlandys Salgado AGH Univ Sci & Technol Ave A Mickiewicza 30 PL-30059 Krakow Poland

Self-learning from historical data and using the resulting information to improve the future self-decision-making focused on technical objects, results machine learning approach, is a major goal in today's devices exploitation process, including also self-maintenance. Simulation-based risk-oriented approach into the broad machine learning machinery usually fits independent theorical distributions to historical degradation data of the device in order to forecast risk indicators and evaluate in the final stage the maintenance process standards The study case of the paper are critical overhead cranes operating with hazardous and continuous operation conditions. The subject of the paper is a technical devices degradation self-analysis for self-maintenance strategy discussed on crane case study, also including device representative data sensitivity analysis. The proposed subject directly impacts the device risk control management and maintenance scheduling processes, and the analysis proves the robustness of the intelligent self-analysis system by introducing additional information for future decision making The devices degradation representative data analysis impact on the preferred exploitation focused decisions have been discussed. The device features have been presented via stochastic non-linear optimization model with bounded constraint that assess a risk global -system indicator based on Monte Carlo simulations. Copyright (C) 2021 The Authors.

关键词： Real-time algorithms scheduling and programming Intelligent maintenance systems Cyber-Physical Systems Robustness analysis Simulation of stochastic systems modeling for control optimization

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