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Q-MPC: stable and efficient reinforcement learning using model predictive control

IFAC-PapersOnLine 2023年第2期56卷 2727-2732页

作者： Tae Hoon Oh Department of Chemical Engineering Kyoto University Kyoto Japan

There is a growing interest in developing an efficient data-driven control method that can be implemented into digitized manufacturing processes. Model-free reinforcement learning (RL) is a machine learning method that can directly learn the optimal control policy from the process data. However, the model-free RL shows higher cost variance than the model-based method and may require an infeasible amount of data to learn the optimal control policy. Motivated by the fact that the system identification to linear model shows high data efficiency and stable performance, this paper proposes combining the linear model predictive control (MPC) with Q-learning. This combined scheme, Q-MPC, can improve the control performance more stably and safely. For the case study, linear MPC, Q-MPC, DDPG, TD3, and SAC methods are applied to the nonlinear benchmark system, mainly focusing on the learning speed and cost variance.

关键词： Model predictive and optimization-based control Reinforcement learning control Process modeling and identification data-based control Learning for control Predictive control

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A MATLAB toolbox for training and implementing physics–guided neural network–based feedforward controllers

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IFAC-PapersOnLine 2023年第2期56卷 4068-4073页

作者： M. Bolderman M. Lazar H. Butler Control Systems Group Eindhoven University of Technology The Netherlands ASML Veldhoven The Netherlands

Physics-guided neural networks (PGNNs) enable accurate identification of inverse system dynamics by effectively embedding a known physical model within a neural network (NN), and thereby achieve high performance when implemented as feedforward controllers. However, training PGNNs using existing NN toolboxes is complicated. Therefore, this paper presents a MATLAB toolbox that systematically implements, trains, and validates PGNNs. Dedicated functions implement recent results that have been proposed in literature, i.e., we ensure that the PGNN converges to a value of the cost function that is strictly upperbounded by the value obtained when using only the physical model, while also imposing a form of graceful degradation when the trained PGNN is used on data that was not present in the training data. The toolbox is available at: https://***/mbolderman/PGNN-Toolbox/ .

关键词： Mechatronics motion control systems data-based control software for system identification identification for control

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data-driven nonlinear predictive control for feedback linearizable systems

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IFAC-PapersOnLine 2023年第2期56卷 617-624页

作者： Mohammad Alsalti Victor G. Lopez Julian Berberich Frank Allgöwer Matthias A. Müller Leibniz University Hannover Institute of Automatic Control 30167 Hannover Germany University of Stuttgart Institute for Systems Theory and Automatic Control 70550 Stuttgart Germany

We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multi-step robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.

关键词： data-based control Nonlinear predictive control data-driven predictive control Feedback linearization

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An Online Adaptation Strategy for Direct data-driven control

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IFAC-PapersOnLine 2023年第2期56卷 644-649页

作者： Johannes Teutsch Sebastian Ellmaier Sebastian Kerz Dirk Wollherr Marion Leibold

The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measurement noise. In this work, we strive to extend the applicability of this paradigm to more strongly nonlinear systems by updating the system representation during control. Unlike existing approaches, our method does not impose suitable excitation to the control inputs, but runs as an observer parallel to the controller. Whenever a rank condition is deemed to be fulfilled, the system representation is updated using newly available datapoints. In a reference tracking simulation of a two-link robotic arm, we showcase the performance of the proposed strategy in a predictive control framework.

关键词： data-based control data-driven optimal control Uncertain systems Predictive control Online adaptation Identification for control

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A Value Iteration Approach to Adaptive Optimal control of Linear Time-Delay Systems

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IFAC-PapersOnLine 2023年第2期56卷 6964-6969页

作者： Leilei Cui Bo Pang Zhong-Ping Jiang

This paper studies the adaptive optimal control for linear time-delay systems described by delay differential equations (DDEs). A key strategy is to exploit the value iteration (VI) approach to solve the linear quadratic optimal control problem for time-delay systems. However, previous learning-based control methods are all exclusively devoted to discrete-time time-delay systems. In this article, we aim to fill in the gap by developing a learning-based VI approach to solve the infinite-dimensional algebraic Riccati equation (ARE) for continuous-time time-delay systems. One nice feature of the proposed VI approach is that an initial admissible controller is not required to start the algorithm. The efficacy of the proposed methodology is demonstrated by the example of autonomous driving.

关键词： data-based control time-delay systems

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Offline Uncertainty Sampling in data-driven Stochastic MPC

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IFAC-PapersOnLine 2023年第2期56卷 650-656页

作者： Johannes Teutsch Sebastian Kerz Tim Brüdigam Dirk Wollherr Marion Leibold

In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-parametric system representation and does not require any prior model identification. The approximation of chance constraints using uncertainty sampling leads to efficient constraint tightening. Under mild assumptions, robust recursive feasibility and closed-loop constraint satisfaction is shown. In a simulation example, we provide evidence for the improved control performance of the proposed control scheme in comparison to a purely robust data-driven predictive control approach.

关键词： data-driven optimal control Stochastic optimal control problems data-based control Predictive control Linear systems Uncertain systems Constrained control

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controllability Maximization of Network Systems: Gradient Computation based on Offline data

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IFAC-PapersOnLine 2023年第2期56卷 10138-10143页

作者： Ikumi Banno Shun-ichi Azuma Ryo Ariizumi Toru Asai Jun-ichi Imura Nagoya University Kyoto University Tokyo Institute of Technology

In the field of network systems, controllability maximization has become more important in terms of efficient control. When an exact model of a network system is not available, data-driven approaches are useful. In this paper, we establish a framework for maximizing the controllability of networked systems by using off-line data. In particular, the maximization with respect to the network topology of the network system is addressed. First, we develop a data-driven method for solving the Lyapunov equation which describes the properties of a system different from the system associated with the data. Second, based on this result, we derive a data-driven method for computing the gradient of a controllability measure (the trace of the controllability Gramian) with respect to the network topology of the network system. Finally, we show that our gradient computation can be used for controllability maximization based on off-line data. The effectiveness of the data-driven methods is numerically demonstrated.

关键词： data-based control optimization controllability Lyapunov equations projected gradient descent network systems

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Scenario-based model predictive control of water reservoir systems

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IFAC-PapersOnLine 2023年第3期56卷 313-318页

作者： Raffaele G. Cestari Andrea Castelletti Simone Formentin Department of Electronics Information and Bioengineering Politecnico di Milano P.za L. Da Vinci 32 20133 Milano Italy

The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy.

关键词： data-based control Robust control control of Constrained Systems Stochastic Optimal control Optimal Operation of Water Resources Systems Uncertainty in Water Resource System control

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data-driven approach for reference shaping considering input constraint 3rd

Data-driven approach for reference shaping considering input...

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3rd IFAC Conference on Modelling, Identification and control of Nonlinear Systems (MiCNON)

作者： Suzuki, Motoya Kaneko, Osamu Isuzu Adv Engn Ctr LTD Res Dept 5 Tsuchidana 8 Fujisawa Kanagawa Japan Univ Electrocommun Grad Sch Informat & Engn 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. Copyright (C) 2021 The Authors.

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 7

Adversarial Learning of Robust and Safe Controllers for Cybe...

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7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS)

作者： Bortolussi, Luca Cairoli, Francesca Carbone, Ginevra Franchina, Francesco Regolin, Enrico Univ Trieste Trieste Italy Saarland Univ Saarbrucken 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. Copyright (C) 2021 The Authors.

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

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