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American Control Conference (ACC)

作者： Marvi, Zahra Kiumarsi, Bahare Michigan State Univ Dept Elect & Comp Engn 428 S Shaw LaneEngn Bldg E Lansing MI 48824 USA

ISBN: (纸本)9781538682661

This paper presents a safe off-policy reinforcement learning (RL) scheme to design optimal controllers for systems with uncertain dynamics. The utility function for which its optimization achieves a desired behavior is augmented with a control barrier function (CBF) candidate providing a platform for merging safety planning and optimal control design. A damping factor is included in the CBF providing a design tool to specify the relative importance of performance and safety. As one of the main contributions of this paper, it is shown that by iterative approximation of the value function, the safety properties of CBF are certified which bridges the broad capability of barrier functions into the learning-based approaches. Then, the safety of control system is proved accordingly. Stability and optimality of the control system in a safe condition are verified. Afterward, an off-policy RL algorithm is used to obtain the safe and optimal controller without requiring full knowledge about the system dynamics. The efficiency of the proposed method is demonstrated on the lane changing as an automotive control problem.

关键词： actor/critic control barrier function safety reinforcement learning adaptive dynamic programming

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Stackelberg Differential Game-based Optimal Fault Estimation and Accommodation for Continuous-time Linear Systems 21st

Stackelberg Differential Game-based Optimal Fault Estimation...

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

作者： Xu, Yuhang Yang, Hao Jiang, Bin Polycarpou, Marios M. Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-1678 Nicosia Cyprus Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

This paper deals with the optimal fault estimation and accommodation problem for a class of linear systems in the framework of Stackelberg differential game theory. In this framework, the observer plays the role of the follower, while the system plays the role of the leader in making sequential decisions. A dual controller approach is used to design an auxiliary controller for the observer such that it can non-cooperate with the controller of the system to achieve the Stackelberg equilibrium. To achieve the online updating of the fault-tolerant controller, an adaptive dynamic programming methodology is used by establishing two critic neural networks for the observer and system respectively. Finally, a simulation is presented to illustrate the efficiency and applicability of the theoretical results. Copyright (C) 2020 The Authors.

关键词： Optimal fault estimation optimal fault-tolerant control Stackelberg differential game adaptive dynamic programming

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Structured Online Learning-based Control of Continuous-time Nonlinear Systems 21st

Structured Online Learning-based Control of Continuous-time ...

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

作者： Farsi, Milad Liu, Jun Univ Waterloo Appl Math Dept Waterloo ON Canada

Model-based reinforcement learning techniques accelerate the learning task by employing a transition model to make predictions. In this paper, a model-based learning approach is presented that iteratively computes the optimal value function based on the most recent update of the model. Assuming a structured continuous-time model of the system in terms of a set of bases, we formulate an infinite horizon optimal control problem addressing a given control objective. The structure of the system along with a value function parameterized in the quadratic form provides a flexibility in analytically calculating an update rule for the parameters. Hence, a matrix differential equation of the parameters is obtained, where the solution is used to characterize the optimal feedback control in terms of the bases, at any time step. Moreover, the quadratic form of the value function suggests a compact way of updating the parameters that considerably decreases the computational complexity. Considering the state-dependency of the differential equation, we exploit the obtained framework as an online learning-based algorithm. In the numerical results, the presented algorithm is implemented on four nonlinear benchmark examples, where the regulation problem is successfully solved while an identified model of the system is obtained with a bounded prediction error. Copyright (C) 2020 The Authors.

关键词： Reinforcement learning Model-based learning Optimal control Feedback control Continuous-time control adaptive dynamic programming Sparse identification

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Neuro-Control for Continuous-Time Stochastic Nonlinear Systems via Online Policy Iteration Algorithm 32

Neuro-Control for Continuous-Time Stochastic Nonlinear Syste...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhou, Tianmin Hou, Jiaxu Li, Handong Di, Zengru Zhao, Bo Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9781728158556

This paper is concerned with the neuro-control for continuous-time nonlinear systems subject to stochastic disturbance. Due to the stochastic disturbance, the traditional value function in existing literature cannot meet the stochastic control problems, since mixed second partial derivatives are employed to construct modified value function of conditional expectation. To solve the Hamilton-Jacobi-Bellman equation, a novel online policy iteration algorithm with an Ito correction term is developed with establishing a critic neural network to approximate the optimal value function. Thus, the online optimal control can be obtained in a closed-loop form. The closed-loop system is guaranteed to be stable in probability via Lyapunov's direct method. Finally, numerical example is provided to illustrate the effectiveness of the developed control method.

关键词： adaptive dynamic programming Reinforcement Learning Policy Iteration Stochastic Nonlinear

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Value-Iteration-Based Neuro-Optimal Tracking Control for Affine Systems with Completely Unknown dynamics 39

Value-Iteration-Based Neuro-Optimal Tracking Control for Aff...

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39th Chinese Control Conference (CCC)

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (纸本)9789881563903

In this paper, the versatile value-iteration-based control method, aimed at affine systems with unknown dynamics, is proposed to deal with the optimal tracking control problem. Neural networks are adopted to approximate system dynamics and a novel approach is presented to estimate the steady state control input based on the established identifier. Additionally, two other neural networks, called the critic network and the action network, are used to implement the optimal tracking control algorithm. Finally, based on the proposed method, the tracking controller is designed to control a specific simulation example. It is shown that, for any randomly given initial state vector, the controller is able to make the affine system track the reference trajectory without knowing the system dynamics.

关键词： adaptive dynamic programming Neuro-Optimal Tracking Control Value Iteration Affine Systems with Unknown dynamics

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Data-driven Structural Control of Monopile Wind Turbine Towers Based on Machine Learning 21st

Data-driven Structural Control of Monopile Wind Turbine Towe...

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

作者： Zhang, Jincheng Zhao, Xiaowei Wei, Xing Univ Warwick Sch Engn Coventry W Midlands England

This paper studies the data-driven structural control of monopile wind turbine towers based on machine learning approach, by using an active tuned mass damper (TMD) located in the nacelle. The adaptive dynamic programming (ADP) approach is employed to obtain the optimal controller which is derived on the modern large-scale machine learning platform Tensorflow. The proposed network structure includes three simple three-layer neural networks (NNs), i.e. a plant network, a critic network, and an action network. The plant network is used to capture the fully nonlinear dynamics of the structural system while the action network is used to approximate the optimal controller. Their training requires the gradient information flowing through the whole network. The automatic differentiation is used in this paper for all the gradient derivations, which greatly improves the employed ADP algorithm's ability in solving complex practical problems. The simulation results of structural control of monopile turbine towers show that on average the active TMD achieves 15% performance improvement on tower fatigue load reduction over a passive TMD, with small active power consumption (less than 0.24% of the turbine's nominal power production). Besides, the controller design considers the trade-off between control performance and power consumption. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming Reinforcement Learning Neural Networks Floating Wind Turbine Active Structural Control

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On Differential Drive Robot Learning Convex Policy with Application to Path-Tracking ⁎

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IFAC-PapersOnLine 2021年第4期54卷 7-12页

作者： A.M. Ribeiro C.H.C. Quiroz A.R. Fioravanti P.R.G. Kurka School of Mechanical Engineering Department of Computational Mechanics University of Campinas São Paulo Brazil School of Mechanical Engineering Department of Mechanical Engineering Pontifical Catholic University of Campinas São Paulo Brazil School of Mechanical Engineering Department of Integrated Systems University of Campinas São Paulo Brazil

This paper presents an experimental validation of a learning convex policy for path-tracking on a differential drive robot. An online implementation of the convex control policy (COCP) is provided in the ROS environment using the CVXGEN package that runs on the on-board computer in a real-time application. The control policies are trained in an off-board computer considering a stochastic kinematic description of the robot and using an approximate gradient method for a given cost-to-go metric function. The policy is validated through simulation and experimental evaluation. In addition, to certify the training efficacy, the experiment is also evaluated using the untuned policy. A discussion regarding trajectory errors is presented as well as final considerations for the solver and real-time concerns.

关键词： adaptive dynamic programming differential-drive robot patch tracking convex optimization control policies learning control

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adaptive Optimal Control of Nonlinear Active Suspension Systems with Completely Unknown dynamics

Adaptive Optimal Control of Nonlinear Active Suspension Syst...

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第33届中国控制与决策会议

作者： Xin Chen Yingbo Huang Jing Na Guanbin Gao Jun Zhao Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology

Ride comfort,tire deflection and suspension stroke are generally conflicting in the active suspension *** achieve a comprom se among the three requirements,a novel online adaptive optimal control method is developed for the nonlinear active suspension systems subject to completely unknown *** on the adaptive dynamic programming(ADP) algorithm,this paper proposes an identifier-critic structure with a dual neural network(NN) to realize the active suspension optimal *** this end,a cost function concerning ride comfort,suspension stroke and control input is ***,an identifier NN with a novel adaptive law based on the parameter estimation error is used to approximate the unknown nonlinear *** obtain the optimal solution,a critic NN with the proposed adaptive law is suggested to design the optimal *** results are provided to verify the effectiveness of the proposed method.

关键词： Active suspension systems optimal control active suspension control adaptive dynamic programming

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adaptive Optimal Tracking Control for Continuous-Time Systems Using Identifier-Critic Based dynamic programming 36

Adaptive Optimal Tracking Control for Continuous-Time System...

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第36届中国控制会议

作者： Dawei Hou Jing Na Yongfeng Lv Guanbin Gao Xing Wu Kunming University Of Science & Technology Faculty Of Mechanical & Electrical Engineering School of Automation Beijing Institute of Technology

ISBN: (纸本)9781538629185

This paper proposes an optimal tracking control of completely unknown affine nonlinear systems using a system augmentation scheme and a modified discounted performance function, where the feedforward and feedback control actions can be obtained simultaneously. We first present an adaptive identifier to estimate the unknown dynamics, where the estimated parameters are updated based on a new adaptive law such that these estimated parameters converge to a small neighborhood around their true values. An augmented system consisting of the tracking error and the generator of the trajectory to be tracked are then constructed, which derives an augmented tracking Hamilton-Jacobi-Bellman（HJB） equation. An online critic neural network（NN） is finally presented to approximate the optimal value function of HJB equation, and then calculate the optimal control action. This leads to an identifier-critic based adaptive dynamic programming（ADP） structure. Simulation results are presented to demonstrate the effectiveness of the proposed method.

关键词： Optimal Tracking Control adaptive Control adaptive dynamic programming System Augmentation

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ADHDP-Based Housing Energy Management For Two Housing Units With Mobile Storage 9

ADHDP-Based Housing Energy Management For Two Housing Units ...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Xue, Jiguang Yan, Chunsheng Liu, Lu Wang, Jiahan Zhao, Xuliang Wang, Xin State Grid Liaoning Elect Power Co Ltd Elect Power Res Inst Shenyang 110006 Peoples R China LiaoNing Elect Power Dev Stock Co LTD Shenyang 110006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781728159225

In this paper, a new optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach is developed to obtain the optimal control law for two-house energy systems. First, the operation modes of the battery are analyzed, and the problem statement is carried out. Next, according to the data of the users, action dependent heuristic dynamic programming (ADHDP) is developed to obtain the optimal control law. Numerical results are given to illustrate the performance of the present method.

关键词： Battery electric vehicle optimal control adaptive dynamic programming battery management

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