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检索条件"主题词=Data-Based Control"

共 197 条记录，以下是51-60 订阅

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An Online Adaptation Strategy for Direct data-driven control 22

An Online Adaptation Strategy for Direct Data-driven Control

引用

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

作者： Teutsch, Johannes Ellmaier, Sebastian Kerz, Sebastian Wollherr, Dirk Leibold, Marion Tech Univ Munich Dept Comp Engn Chair Automat Control Engn LSR Theresienstr 90 D-80333 Munich Germany

ISBN: (纸本)9781713872344

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. Copyright (c) 2023 The Authors.

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 22

A Value Iteration Approach to Adaptive Optimal Control of Li...

引用

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

作者： Cui, Leilei Pang, Bo Jiang, Zhong-Ping New York Univ Tandon Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA

ISBN: (纸本)9781713872344

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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controllability Maximization of Network Systems: Gradient Computation based on Offline data 22

Controllability Maximization of Network Systems: Gradient Co...

引用

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

作者： Banno, Ikumi Azuma, Shun-ichi Ariizumi, Ryo Asai, Toru Imura, Jun-ichi Nagoya Univ Nagoya Aichi Japan Kyoto Univ Kyoto Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781713872344

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 datadriven 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. Copyright (c) 2023 The Authors.

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

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A MATLAB toolbox for training and implementing physics-guided neural network-based feedforward controllers 22

A MATLAB toolbox for training and implementing physics-guide...

引用

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

作者： Bolderman, M. Lazar, M. Butler, H. Eindhoven Univ Technol Control Syst Grp Eindhoven Netherlands

ISBN: (纸本)9781713872344

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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Neural network based adaptive control of gantry cranes 22

Neural network based adaptive control of gantry cranes

引用

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

作者： Otto, E. Maksakov, A. Golovin, I. Palis, S. Otto von Guericke Univ D-39106 Magdeburg Germany IU Int Univ Appl Sci D-99084 Erfurt Germany

ISBN: (纸本)9781713872344

During the operation of large gantry cranes oscillations of flexible structures caused by the trolley movement lead to undesired wear of the construction and deteriorate the efficiency of the crane operation. Therefore, a neural network based adaptive control system for tracking control of the payload position and damping of oscillations is presented and validated in numerical simulations. By comparison to a previously derived nonlinear controller, it is shown that the proposed control law achieves good tracking and damping results with minimal a priori knowledge of the crane dynamics and without knowledge about specific plant parameters, therefore reducing the required modeling effort significantly.

关键词： adaptive and learning systems nonlinear adaptive control data-based control lyapunov methods system identification and adaptive control of distributed parameter systems

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Towards data-Driven Predictive control Using Wavelets 22

Towards Data-Driven Predictive Control Using Wavelets

引用

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

作者： Sathyanarayanan, Kiran Kumar Pan, Guanru Faulwasser, Timm TU Dortmund Univ Inst Energy Syst Energy Efficiency & Energy Econ D-44227 Dortmund Germany

Towards data-Driven Predictive control Using Wavelets

ISBN: (纸本)9781713872344

Towards data-Driven Predictive control Using Wavelets

关键词： data-driven optimal control predictive control data-based control wavelet transform.

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Optimal Economic Gas Turbine Dispatch with Deep Reinforcement Learning 22

Optimal Economic Gas Turbine Dispatch with Deep Reinforcemen...

引用

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

作者： Sage, Manuel Staniszewski, Martin Zhao, Yaoyao Fiona McGill Univ Dept Mech Engn Montreal PQ Canada Siemens Energy Canada Ltd Montreal PQ Canada

ISBN: (纸本)9781713872344

Dispatching strategies for gas turbines (GTs) are changing in modern electricity grids. A growing incorporation of intermittent renewable energy requires GTs to operate more but shorter cycles and more frequently on partial loads. Deep reinforcement learning (DRL) has recently emerged as a tool that can cope with this development and dispatch GTs economically. The key advantages of DRL are a model-free optimization and the ability to handle uncertainties, such as those introduced by varying loads or renewable energy production. In this study, three popular DRL algorithms are implemented for an economic GT dispatch problem on a case study in Alberta, Canada. We highlight the benefits of DRL by incorporating an existing thermodynamic software provided by Siemens Energy into the environment model and by simulating uncertainty via varying electricity prices, loads, and ambient conditions. Among the tested algorithms and baseline methods, Deep Q-Networks (DQN) obtained the highest rewards while Proximal Policy Optimization (PPO) was the most sample efficient. We further propose and implement a method to assign GT operation and maintenance cost dynamically based on operating hours and cycles. Compared to existing methods, our approach better approximates the true cost of modern GT dispatch and hence leads to more realistic policies. Copyright (c) 2023 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： reinforcement learning data-based control intelligent control of power systems optimal operation and control of power systems

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Offline Uncertainty Sampling in data-driven Stochastic MPC 22

Offline Uncertainty Sampling in Data-driven Stochastic MPC

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

作者： Teutsch, Johannes Kerz, Sebastian Brudigam, Tim Wollherr, Dirk Leibold, Marion Tech Univ Munich Dept Comp Engn Chair Automat Control Engn LSR Theresienstr 90 D-80333 Munich Germany

ISBN: (纸本)9781713872344

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. Copyright (c) 2023 The Authors.

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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Q-MPC: stable and efficient reinforcement learning using model predictive control 22

Q-MPC: stable and efficient reinforcement learning using mod...

引用

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

作者： Oh, Tae Hoon Kyoto Univ Dept Chem Engn Kyoto Japan

ISBN: (纸本)9781713872344

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. Copyright (c) 2023 The Authors.

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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Design of a database-Driven controller Realized via CMAC Memory 22

Design of a Database-Driven Controller Realized via CMAC Mem...

引用

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

作者： Li, Zhifeng Yamamoto, Toru Hiroshima Univ Grad Sch Adv Sci & Engn Hiroshima Japan

ISBN: (纸本)9781713872344

Proportional-integral-derivative (PID) control is widely applied in industries, though it is sometimes difficult to apply to nonlinear systems. In contrast, database-driven PID (DDPID) control has been proposed as a control method to adjust adaptive PID parameters. However, although this method can efficiently construct control systems with high control performance for nonlinear systems, its large computational cost in terms of computation time and memory consumption limits its application to relatively slow-response systems such as some process control systems. Therefore, in this paper, the cerebellar model articulation controller (CMAC) is introduced, and a new method to realize the database constructed by DD-PID with CMAC memory is proposed. Finally, numerical simulation examples are employed to quantitatively evaluate the reduction of computation time and memory usage, which verifies the effectiveness of the proposed method. Copyright (c) 2023 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： control System Design data-based control data-Driven Optimal control Output Feedback control Intelligent control

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