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

作者： Garcia, Cristiane Silva Bazanella, Alexandre Sanfelici Univ Fed Rio Grande do Sul Dept Automat & Energy Porto Alegre RS Brazil

Virtual Reference Feedback Tuning (VRFT) is a direct data-driven control design method employed to tune a controller's parameters aiming to achieve a prescribed closed-loop performance. Its primary formulation leads to a biased estimate in the presence of noise, so an instrumental variable (IV) alternative has been proposed and this alternative has been favoured whenever the noise level is significant. Even though VRFT thus formulated has been very successful, the bias reduction through the IV approach comes at the cost of an important increase in the variance of the parameters' estimate. In this work we propose a different solution for the parameters estimation in VRFT which reduces bias without increasing the variance-the Constrained Total Least Squares (CTLS). The effectiveness of the proposed solution is illustrated by three case studies, showing that the mean square error of the parameters' estimate is smaller when compared to previously proposed solutions and, most importantly, that the closed-loop performance is significantly better. Copyright (C) 2020 The Authors.

关键词： data-based control model following control virtual reference feedback tuning errors in variables identification constrained total least squares

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data-driven parameterizations of suboptimal LQR and H2 controllers 21st

Data-driven parameterizations of suboptimal LQR and H2 contr...

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

作者： van Waarde, Henk J. Mesbahi, Mehran Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Groningen Netherlands Univ Groningen Engn & Technol Inst Groningen Groningen Netherlands Univ Washington William E Boeing Dept Aeronaut & Astronaut Seattle WA 98195 USA

In this paper we design suboptimal control laws for an unknown linear system on the basis of measured data. We focus on the suboptimal linear quadratic regulator problem and the suboptimal H-2 control problem. For both problems, we establish conditions under which a given data set contains sufficient information for controller design. We follow up by providing a data-driven parameterization of all suboptimal controllers. We will illustrate our results by numerical simulations, which will reveal an interesting trade-off between the number of collected data samples and the achieved controller performance. Copyright (C) 2020 The Authors.

关键词： data-based control optimal control theory linear systems

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data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information 21st

Data-driven Energy Management Strategy for Plug-in Hybrid El...

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

作者： Choi, Yongkeun Guanetti, Jacopo Moura, Scott Borrelli, Francesco Univ Calif Berkeley Dept Mech Engn Model Predict Control Lab Berkeley CA 94720 USA Univ Calif Berkeley Dept Civil & Environm Engn Energy Controls & Applicat Lab Berkeley CA 94720 USA

This paper presents a data-driven supervisory energy management strategy (EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloud connectivity to increase energy efficiency by learning control policies from completed trips. The proposed EMS consists of two layers, a cloud layer and an on-board layer. The cloud layer has two main tasks: the first task is to learn EMS policy parameters from historical trip data, and the second task is to provide the policy parameters along a certain route requested from the vehicle. The onboard layer receives the learned policy parameters from the cloud layer and computes a real-time solution to the powertrain energy management problem, using a model predictive control scheme. The proposed EMS is evaluated on more than 3000 miles (48 independent driving cycles) of real-world trip data, collected along three commuting routes in California. For the routes, the proposed algorithm shows 3.3%, 7.3%, and 6.5% improvement in average MPGe when compared to a baseline EMS. Copyright (C) 2020 The Authors.

关键词： data-based control Nonlinear predictive control Real-time control Engine modelling and control Hybrid and alternative drive vehicles Nonlinear and optimal automotive control

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LSTM-based IMC approach applied in Wastewater Treatment Plants: performance and stability analysis 21st

LSTM-based IMC approach applied in Wastewater Treatment Plan...

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

作者： Pisa, I Morell, A. Vicario, J. L. Vilanova, R. Univ Autonoma Barcelona Wireless Informat Networking Win Grp Bellaterra Spain Univ Autonoma Barcelona Adv Syst Automat & Control ASAC Grp Bellaterra Spain

Wastewater treatment plants are industries where the reduction of residual water pollutant concentrations is performed. These kind of industries are characterised by applying highly complex and nonlinear biochemical and biological processes. Thus, some of the concentrations involved in these processes have to be controlled to assure that they are maintained at a given set-point. For that reason, different control strategies such as Proportional Integral (PI) controllers, Model Predictive controllers (MPC), Fuzzy Logic or Internal Model controllers (IMC) have been applied during the last years. However, the appearance of Artificial Neural Networks (ANNs) is changing this scenario. They have been adopted to predict certain WWTP parameters and then feed conventional controllers or even to implement some of them. Here, an IMC approach implemented uniquely with Long Short-Term Memory (LSTM) cells to model the direct and inverse models of the process under control is proposed. Furthermore, its stability conditions are computed adopting a data-based test since no mathematical expressions of the different models are considered. Results show that this approach is stable in the frequency region where it is operating. Besides, control performance shows that this IMC is able to significantly improve the Benchmark Simulation Model No.1 default PI control strategy. Copyright (C) 2020 The Authors.

关键词： Artificial Neural Networks data-based control Internal Model control Robust control Wastewater Treatment processes

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Iterative learning control not to interfere with feedback control ⁎

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IFAC-PapersOnLine 2021年第14期54卷 460-463页

作者： Yusuke Fujimoto The University of Kitakyushu Kitakyushu Fukuoka 8080135 Japan

This paper discusses about Iterative Learning control (ILC) which learns an appropriate feedforward input from iterative experiments. In particular, this paper focuses on systems which include integrator, and considers tracking to a desired output which converges to a constant. In this case, the feedforward input should converges to zero, however classical ILC does not take this condition into account, and interfere with the feedback controller. This paper proposes a new design of ILC whose resulting input converges to zero exponentially. The effectiveness of the proposed method is demonstrated by a practical experiment with a flexible arm. Since a flexible arm is known to nonlinear, the proposed method is expected to be effective for nonlinear systems.

关键词： Iterative learning control data-based control tracking

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Robust Reinforcement Learning for Stochastic Linear Quadratic control with Multiplicative Noise ⁎

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IFAC-PapersOnLine 2021年第7期54卷 240-243页

作者： Bo Pang Zhong-Ping Jiang Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University Six Metrotech Center Brooklyn NY 11201 USA

This paper studies the robustness of reinforcement learning for discrete-time linear stochastic systems with multiplicative noise evolving in continuous state and action spaces. As one of the popular methods in reinforcement learning, the robustness of policy iteration is a longstanding open issue for the stochastic linear quadratic regulator (LQR) problem with multiplicative noise. A solution in the spirit of small-disturbance input-to-state stability is given, guaranteeing that the solutions of the policy iteration algorithm are bounded and enter a small neighborhood of the optimal solution, whenever the error in each iteration is bounded and small. In addition, a novel off-policy multiple-trajectory optimistic least-squares policy iteration algorithm is proposed, to learn a near-optimal solution of the stochastic LQR problem directly from online input/state data, without explicitly identifying the system matrices. The efficacy of the proposed algorithm is supported by rigorous convergence analysis and numerical results on a second-order example.

关键词： Reinforcement learning control Stochastic optimal control problems data-based control Robustness analysis Input-to-state stability

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Stable Gaussian process based tracking control of Euler-Lagrange systems

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AUTOMATICA 2019年 103卷 390-397页

作者： Beckers, Thomas Kulic, Dana Hirche, Sandra Tech Univ Munich Dept Elect & Comp Engn Chair Informat Oriented Control ITR Arcisstr 21 D-80333 Munich Germany Univ Waterloo Dept Elect & Comp Engn Adapt Syst Lab Waterloo ON N2L 3G1 Canada

Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking error can be reduced either by increasing the feedback gains or improving the model of the system. The latter is clearly preferable as it allows to maintain good tracking performance at low feedback gains. However, accurate models are often difficult to obtain. In this article, we address the problem of stable high-performance tracking control for unknown Euler-Lagrange systems. In particular, we employ Gaussian Process regression to obtain a data-driven model that is used for the feed-forward compensation of unknown dynamics of the system. The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence. The proposed control law guarantees a globally bounded tracking error with a specific probability. Simulation studies demonstrate the superiority over state of the art tracking control approaches. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： data-based control Nonparametric methods Stochastic control Stability of nonlinear systems Adaptive system and control Robotic manipulators

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data-driven approximate Q-learning stabilization with optimality error bound analysis

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AUTOMATICA 2019年 103卷 435-442页

作者： Li, Yongqiang Yang, Chengzan Hou, Zhongsheng Feng, Yuanjing Yin, Chenkun Zhejiang Univ Technol Coll Informat Engn Hangzhou Zhejiang Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing Peoples R China

The approximate Q-learning (AQL), as a typical reinforcement learning method, has attracted extensive attention in the past few years because of its outstanding ability to solve the nonlinear optimal control problem when the knowledge/model of the plant is unavailable. However, because of function approximation errors, the AQL algorithms can just give a near-optimal solution. Hence, a quantitative analysis result of the optimality error bound has important significance. In this paper, the off-line value iteration AQL is used to solve the model-free optimal stabilization control problem and a new optimality error bound analysis framework is proposed. Firstly, for convenience and clearness of analyzing the optimality error bound, the Q-learning operator is well defined based on the estimate of the domain of attraction (DOA) for closed-loops. Secondly, a quantitative analysis result of the estimation error bound for the optimal Q-function is obtained by selecting the function estimator as Gaussian processes regression. Finally, a quantitative analysis result of the optimality error bound, which is the error bound between the optimal cost and the actual cost of the AQL closed-loop, is given. As shown in the main result of this paper, the optimality error bound is determined by the approximation error bound of the function estimator (due to the finite number of data points) and the difference between the two Q functions obtained in the last two iterations (due to the finite number of iterations). (C) 2019 Elsevier Ltd. All rights reserved.

关键词： data-based control Reinforcement learning Q-learning Domain of attraction Asymptotic stabilization

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Kalman-Filtering-based Iterative Feedforward Tuning in Presence of Stochastic Noise: With Application to a Wafer Stage

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第11期15卷 5816-5826页

作者： Li, Li Liu, Yang Li, Liyi Tan, Jiubin Harbin Inst Technol Minist Ind & Informat Technol Key Lab Ultraprecis Intelligent Instrumentat Harbin 150080 Heilongjiang Peoples R China Harbin Inst Technol Ctr Ultraprecis Optoelect Instrument Engn Harbin 150080 Heilongjiang Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Heilongjiang Peoples R China

Iterative feedforward tuning (IFFT) enables high performance for motion systems that perform varying tasks without the need for system models. In this paper, IFFT is employed for a wafer stage to achieve good trajectory tracking performance and excellent disturbance compensation ability. Recently, the instrumental variable (IV) approach has been introduced into IFFT algorithms (IV-IFFT), enabling unbiased estimates for the parameters of a feedforward controller in the presence of stochastic noise. However, the estimation variances achievable with IV-IFFT are larger than zero. The aim of this paper is to develop an IFFT algorithm that enables unbiased estimates with zero asymptotic variances, which can be achieved by the simultaneous use of the Kalman filtering (KF) approach and the IV approach in IFFT, yielding the KF-IV-IFFT algorithm. The different roles of KF and IV approaches to improve the noise-tolerant capability of IFFT are also revealed. Experimental results obtained on a wafer stage confirm the practical relevance of the proposed KF-IV-IFFT algorithm.

关键词： Task analysis Informatics Feedforward systems Estimation Adaptation models Semiconductor device modeling Tuning data-based control feedforward control (FFC) iterative feedforward tuning (IFFT) iterative learning control (ILC) motion control

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data-driven robust stabilization with robust domain of attraction estimate for nonlinear discrete-time systems

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AUTOMATICA 2020年 119卷 109031-109031页

作者： Li, Yongqiang Lu, Chaolun Hou, Zhongsheng Feng, Yuanjing Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Qingdao Univ Sch Automat Qingdao Peoples R China

Nonlinear robust control is pursued by overcoming the drawback of linear robust control that it ignores available information about existing nonlinearities and the resulting controllers may be too conservative, especially when the nonlinearities are significant. However, most existing nonlinear robust control approaches just consider the affine nonlinear nominal model and thereby ignore available information about existing non-affine nonlinearities. When the general nonlinear nominal model is considered, the robust domain of attraction (RDOA) of closed-loops requires extensive investigation because it is hard to achieve the global stabilization. In this paper, we propose a new nonlinear robust control method based on Lyapunov function to stabilize a discrete-time uncertain system and to estimate the RDOA of closed-loops. First, a sufficient condition for robust stabilization of all plants in a plant set and estimation of the RDOA of all closed-loops is proposed. Then, to tackle the non-affine nonlinearities, a data-driven method of estimating the robust negative-definite domains (RNDD) is presented, and based on it the estimation of the RDOA of closed-loops and the resulting controller design are also given. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： data-based control Robust control of nonlinear systems Asymptotic stabilization Domain of attraction

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