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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Valderrama, Freddy Ruiz, Fredy Vicino, Antonio Garulli, Andrea Pontificia Univ Javeriana Dept Elect Bogota Colombia Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Univ Siena DIISM Siena Italy

The Youla-Kucera parametrization is a fundamental result in system theory, very useful when designing model-based controllers. In this paper, this parametrization is employed to solve the controller design from data problem, without requiring a process model. It is shown that employing the proposed controller structure it is possible to achieve more stringent closed-loop performances than previous works in literature, maintaining a criterion to estimate the closed-loop stability. The developed design methodology does not imply a plant identification step and the solution can be obtained by least-squares algorithms in the case of stochastic additive noise. The designed solution is evaluated through Monte Carlo simulations for the regulation problem of an under-damped system. Copyright (C) 2020 The Authors.

关键词： data-based control Identification for control Uncertain systems Parametric optimization

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Probabilistic H2-norm estimation via Gaussian process system identification 21st

Probabilistic H2-norm estimation via Gaussian process system...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Persson, Daniel Koch, Anne Allgower, Frank Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany

We present a method for data-based estimation of the H-2-norm of a linear time-invariant system from input-output data in a probabilistic setting by employing the recent advances in Gaussian process system identification using stable-spline kernels. Advantages of this starting point include that the norm can be estimated for the continuous-time system and over infinite horizon, even though only a finite number of measurements are available. We approximate the H-2-norm distribution as Gaussian, whose expectation can even be obtained analytically, while we use a numerical scheme based on Gaussian process quadrature for the variance. Not only do we utilize the posterior variance of the Gaussian process to derive an error estimate for the H-2-norm, but also to tune the estimation by optimizing the input sequence. The performance of the developed scheme is thoroughly evaluated in simulation. Copyright (C) 2020 The Authors.

关键词： Nonparametric methods Bayesian methods data-based control Identification for control Input and excitation design

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Multivariable Correlation-based Tuning for Load Disturbance Rejection 21st

Multivariable Correlation-based Tuning for Load Disturbance ...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： da Silva, Roger W. P. Eckhard, Diego Univ Fed Rio Grande do Sul Dept Sistemas Eletr Automacao & Energia Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Dept Matemat Pura & Aplicada Porto Alegre RS Brazil

In many industrial processes, the setpoint signals present few changes and variations in the output are due mainly to the disturbances that enter the closed-loop and may be understood as load disturbances. Even though this is a common problem, the response to load disturbances is a topic not well covered in the direct data-driven control literature. This work seeks to fill that gap presenting a direct data-driven method to tune a multivariable controller in order to achieve certain load disturbance response described by some reference model. This work extends a previous one dealing with the monovariable case and uses the same correlation approach employed before in the Correlation-based Tuning (CbT). Copyright (C) 2020 The Authors.

关键词： data-based control model following control disturbance rejection linear multivariable systems parametric optimisation

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data-driven approach for reference shaping considering input constraint

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IFAC-PapersOnLine 2021年第14期54卷 227-232页

作者： Motoya Suzuki Osamu Kaneko Research 5 dept. Isuzu advanced engineering center LTD. Tsuchidana 8 Kanagawa Japan Graduate school of informatics and engineering University of Electro-communications Chofu-gaoka 1-5-1 Tokyo Japan

data-driven reference shaping method was proposed by Kuwabara et al. (2020). to realize the desired output by shaping the reference signal without any changes of the implemented controller. In the case where the input signal becomes large, such a desired output cannot be realized and an excessive input power leads to crucial failure of the closed loop. To prevent such situations, it is important to take the input into account in the reference shaping design. From these backgrounds, this paper expands the result on data-driven reference shaping by Kuwabara et al. (2020) so as to address the input limitation explicitly. The main key point of the proposed method here is to introduce the saturation element in the reference shaping. The validity of the proposed method is also investigated to show experiment verifications.

关键词： control with limited information data-based control control of constrained systems data-driven reference shaping

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Adversarial Learning of Robust and Safe controllers for Cyber-Physical Systems

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IFAC-PapersOnLine 2021年第5期54卷 223-228页

作者： Luca Bortolussi Francesca Cairoli Ginevra Carbone Francesco Franchina Enrico Regolin University of Trieste Italy Saarland University Saarbrücken Germany

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies.

关键词： Robust control Signal Temporal Logic Adversarial Learning data-based control Test generation Safe control

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Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems 21st

Task Decomposition for MPC: A Computationally Efficient Appr...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Vallon, Charlott Borrelli, Francesco Univ Calif Berkeley Berkeley CA 94701 USA

Task Decomposition method for iterative learning Model Predictive control (TDMPC) for linear time-varying systems is presented. We consider the availability of state-input trajectories which solve an original task T1, and design a feasible MPC policy for a new task, T2, using stored data from T1. Our approach applies to tasks T2 which are composed of subtasks contained in T1. In this paper we formally define the task decomposition problem, and provide a feasibility proof for the resulting policy. The proposed algorithm reduces the computational burden for linear time-varying systems with piecewise convex constraints. Simulation results demonstrate the improved efficiency of the proposed method on a robotic path-planning task. Copyright (C) 2020 The Authors.

关键词： data-based control Iterative and Repetitive Learning control Linear Model Predictive control Convex Optimization

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data-based guarantees of set invariance properties 21st

Data-based guarantees of set invariance properties

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Bisoffi, Andrea De Persis, Claudio Tesi, Pietro Univ Groningen ENTEG NL-9747 AG Groningen Netherlands Univ Groningen JC Willems Ctr Syst & Control NL-9747 AG Groningen Netherlands Univ Florence DINFO I-50139 Florence Italy

For a discrete-time linear system, we use data from an open-loop experiment to design directly a linear feedback controller enforcing that a given (polyhedral) set of the state is invariant and given (polyhedral) constraints on the control are satisfied. By building on classical results from model-based set invariance and a fundamental result from Willems et al., the controller designed from data has the following desirable features. The satisfaction of the above properties is guaranteed only from data, it can be assessed by solving a numerically-efficient linear program, and, under a certain rank condition, a data-based solution is feasible if and only if a model-based solution is feasible. Copyright (C) 2020 The Authors.

关键词： data-based control control of constrained systems Constrained control Linear Systems Linear programming Convex optimization

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Maximum Likelihood Methods for Inverse Learning of Optimal controllers 21st

Maximum Likelihood Methods for Inverse Learning of Optimal C...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Menner, Marcel Zeilinger, Melanie N. Swiss Fed Inst Technol Inst Dynam Syst & Control Zurich Switzerland

This paper presents a framework for inverse learning of objective functions for constrained optimal control problems, which is based on the Karush-Kuhn-Tucker (KKT) conditions. We discuss three variants corresponding to different model assumptions and computational complexities. The first method uses a convex relaxation of the KKT conditions and serves as the benchmark. The main contribution of this paper is the proposition of two learning methods that combine the KKT conditions with maximum likelihood estimation. The key benefit of this combination is the systematic treatment of constraints for learning from noisy data with a branch-and-bound algorithm using likelihood arguments. This paper discusses theoretic properties of the learning methods and presents simulation results that highlight the advantages of using the maximum likelihood formulation for learning objective functions. Copyright (C) 2020 The Authors.

关键词： Learning for control data-based control constrained control

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data-Driven Tracking MPC for Changing Setpoints 21st

Data-Driven Tracking MPC for Changing Setpoints

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Berberich, Julian Koehler, Johannes Mueller, Matthias A. Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany Leibniz Univ Hannover Inst Automat Control D-30167 Hannover Germany

We propose a data-driven tracking model predictive control (MPC) scheme to control unknown discrete-time linear time-invariant systems. The scheme uses a purely data-driven system parametrization to predict future trajectories based on behavioral systems theory. The control objective is tracking of a given input-output setpoint. We prove that this setpoint is exponentially stable for the closed loop of the proposed MPC, if it is reachable by the system dynamics and constraints. For an unreachable setpoint, our scheme guarantees closed-loop exponential stability of the optimal reachable equilibrium. Moreover, in case the system dynamics are known, the presented results extend the existing results for model-based setpoint tracking to the case where the stage cost is only positive semidefinite in the state. The effectiveness of the proposed approach is illustrated by means of a practical example. Copyright (C) 2020 The Authors.

关键词： Predictive control data-based control tracking

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Machine Learning Assisted Solutions of Mixed Integer MPC on Embedded Platforms 21st

Machine Learning Assisted Solutions of Mixed Integer MPC on ...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Loehr, Yannik Klauco, Martin Fikar, Miroslav Moennigmann, Martin Ruhr Univ Bochum Automat Control & Syst Theory Bochum Germany Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Bratislava Slovakia

Many control applications, especially in the field of energy systems, require a simultaneous decision for continuous and binary values of control inputs. In optimal control methods like model predictive control (MPC), this leads to the problem of solving expensive mixed-integer programs online. As this solution in practice has to be calculated with low cost embedded hardware with low energy demand, it is necessary to reduce the computational demand in advance. We present an approach to replacing the mixed-integer program by a simpler quadratic program by means of learning techniques. To be more specific, we design a neural network and a support vector machine to classify the optimal control policies for the binary inputs offline and evaluate this decision in the online step as the basis for the solution of the quadratic program. As a result, we achieve a controller suitable for implementation on embedded hardware. We demonstrate its applicability to a domestic heating system. The results indicate a very high quality of the approximation of the primary optimal controller that solves mixed-integer programs online. Copyright (C) 2020 The Authors.

关键词： nonlinear predictive control energy control data-based control neural networks classification heat flows control applications

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