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reinforcement learning-Based Minimum Energy Position Control of Dielectric Elastomer Actuators

ieee TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2021年第4期29卷 1674-1688页

作者： Massenio, Paolo Roberto Rizzello, Gianluca Comitangelo, Giuseppe Naso, David Seelecke, Stefan Polytech Univ Bari Dept Elect & Informat Engn I-70126 Bari Italy Saarland Univ Dept Syst Engn D-66123 Saarbrucken Germany Saarland Univ Dept Mat Sci & Engn D-66123 Saarbrucken Germany GEM ICT Res & Dev Srl I-70126 Bari Italy

This article deals with the closed-loop optimal control of mechatronic devices based on dielectric elastomer membranes. The goal is to minimize the input electrical energy required to achieve a given position regulation task. The actuator is modeled based on a free-energy framework, which provides a thermodynamically consistent characterization of the losses that occur during actuation. Due to the strongly nonlinear behavior of both system model and dissipation function, traditional techniques based on the analytical solution of the Hamilton-Jacobi-Bellman (HJB) equation cannot be applied. Therefore, a reinforcement learning-based algorithm is here proposed as a tool to solve, offline, the HJB equation related to the energy minimization problem. After discussing the theory, experimental results are presented to validate the effectiveness of the proposed approach for different positioning tasks.

关键词： Optimal control Actuators Mathematical model Task analysis Springs Position control Minimization adaptive dynamic programming (ADP) dielectric elastomer (DE) actuators energy minimization mechatronics optimal control reinforcement learning (RL)

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Hybrid Iteration ADP Algorithm to Solve Cooperative, Optimal Output Regulation Problem for Continuous-Time, Linear, Multiagent Systems: Theory and Application in Islanded Modern Microgrids With IBRs

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第1期71卷 834-845页

作者： Qasem, Omar Davari, Masoud Gao, Weinan Kirk, Daniel R. Chai, Tianyou Florida Inst Technol Coll Engn & Sci Melbourne FL 32901 USA Georgia Southern Univ Dept Elect & Comp Engn Statesboro Campus Statesboro GA 30460 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

In this article, we propose a novel adaptive dynamic programming (ADP) algorithm, named hybrid iteration (HI), to solve the cooperative, optimal output regulation problem ((CORP)-R-2) for continuous-time, linear, multi-agent systems. Unlike the traditional ADP algorithms, i.e., policy iteration (PI) and value iteration (VI), HI does not need an initial stabilizing control policy required by PI. At the same time, it maintains a faster convergence rate compared with VI. First, a model-based HI algorithm is proposed to solve the (CORP)-R-2. Based on the proposed HI algorithm, a data-driven, adaptive, optimal controller is developed to solve the cooperative, adaptive, and optimal output regulation problem without using any information about the physics of the system. Instead, the states/input information collected along the trajectories of the dynamic system is employed. The proposed data-driven HI is applied to the adaptive, optimal secondary voltage control (also known as voltage restoration control) of an islanded modern microgrid based on the inverter-based resources. Compared with the VI and PI algorithms, comparative simulation results demonstrate that the proposed HI approach is significantly able to save the convergence time of the central processing unit (also known as CPU) deployed, reduce the number of learning iterations, and remove the requirement of the initial stabilizing control policy. Comparative experiments reveal the practicality and superiority of the proposed methodology.

关键词： Regulation Convergence Heuristic algorithms Symmetric matrices Optimal control Multi-agent systems Microgrids adaptive dynamic programming (ADP) continuous-time cooperative linear multiagent systems (MASs) optimal output regulation reinforcement learning

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Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Electric Vehicles Using Deep reinforcement learning With Action Trimming

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ieee TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第7期71卷 7171-7185页

作者： Fu, Zhumu Wang, Haocong Tao, Fazhan Ji, Baofeng Dong, Yongsheng Song, Shuzhong Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Peoples R China Henan Univ Sci & Technol Henan Key Lab Robot & Intelligent Syst Luoyang 471023 Peoples R China

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7%.

关键词： Dater driven deep reinforcement learning energy management strategy fuel cell hybrid electric vehicle heuristic technique

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dynamic Pricing by Multiagent reinforcement learning

Dynamic Pricing by Multiagent Reinforcement Learning

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International symposium on Electronic Commerce and Security

作者： Han, Wei Liu, Lingbo Zheng, Huaili Nanjing Univ Finance & Econ Informat Engn Coll Nanjing 210046 Peoples R China

ISBN: (纸本)9780769532585

dynamic pricing in electronic marketplaces is a basic problem in electronic commercial. In multiagent environments, the optimal pricing policy of agent depends on the pricing policies of other agents. This makes the learning problem more problematic. This paper proposes an efficient online learning algorithm, which integrates the observed objective actions as well as the subjective inferential intention of the opponents. by establishing the decision model of other agents and predicting their proposed price in advance, agent becomes adaptive to its opponents and can make good decisions in long terms. The algorithm is proven to be effective when coming to the problem of seller pricing in electronic marketplaces.

关键词： reinforcement learning

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A Neural Architecture to Address reinforcement learning Problems

A Neural Architecture to Address Reinforcement Learning Prob...

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International Joint Conference on Neural Networks (IJCNN)

作者： de Arruda, Rodrigo L. S. Von Zuben, Fernando J. Univ Campinas UNICAMP Sch Elect & Comp Engn FEEC Dept Comp Engn & Ind Automat DCA Lab Bioinformat & Bioinspired Comp LBiC Campinas SP Brazil

ISBN: (纸本)9781424496365

In this paper, the reinforcement learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel adaptive dynamic programming algorithm which is based on a Multi-layer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm.

关键词： Approximation methods dynamic programming Equations Heuristic algorithms Markov decision process Markov processes Markov processes Mathematical model Monte Carlo methods adaptive dynamic programming automatic control dynamic programming inverted pendulum learning (artificial intelligence) learning algorithm multilayer perceptron neural network multilayer perceptrons neural architecture neural net architecture parameterized function approximator reinforcement learning wire-fitting

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Barrier Lyapunov Function-Based Safe reinforcement learning for Autonomous Vehicles With Optimized Backstepping

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第2期35卷 2066-2080页

作者： Zhang, Yuxiang Liang, Xiaoling Li, Dongyu Ge, Shuzhi Sam Gao, Bingzhao Chen, Hong Lee, Tong Heng Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

关键词： Safety Backstepping Uncertainty Nonlinear systems Autonomous vehicles Lyapunov methods Control systems adaptive dynamic programming (ADP) autonomous vehicles Barrier Lyapunov Function (BLF) safe reinforcement learning (SRL)

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Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of an Irrotational Current

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ieee TRANSACTIONS ON ROBOTICS 2018年第2期34卷 486-496页

作者： Walters, Patrick Kamalapurkar, Rushikesh Voight, Forrest Schwartz, Eric M. Dixon, Warren E. Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Oklahoma State Univ Dept Mech & Aerosp Engn Stillwater OK 74074 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Online approximation of the optimal station-keeping strategy for a marine craft subject to an irrotational current is considered. An approximate policy that minimizes a user-defined cost function over an infinite time horizon is obtained using an actor-critic-identifier-based adaptive dynamic programming technique. The hydrodynamic drift dynamics are assumed to be unknown;therefore, a concurrent learning-based system identifier is developed to identify the unknown model parameters. The identified model is used to implement an adaptive model-based reinforcement learning technique to estimate the unknown value function. The developed policy guarantees uniformly ultimately bounded convergence of the vehicle to the desired station and uniformly ultimately bounded convergence of the approximated policies to the optimal polices without the requirement of persistence of excitation. The developed strategy is validated using an autonomous underwater vehicle, where the three degrees-of-freedom in the horizontal plane are regulated. The experiments are conducted in a second-magnitude spring located in central Florida.

关键词： adaptive dynamic programming (ADP) marine craft nonlinear control station keeping

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Intelligent Energy Management Strategy Based on an Improved reinforcement learning Algorithm With Exploration Factor for a Plug-in PHEV

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8725-8735页

作者： Lin, Xinyou Zhou, Kuncheng Mo, Liping Li, Hailin Fuzhou Univ Coll Mech Engn & Automat Fuzhou 350108 Peoples R China West Virginia Univ Dept Mech & Aerosp Engn Morgantown WV 26506 USA

An intelligent energy management strategy (EMS) based on an improved reinforcement learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy management strategy of a plug-in PHEV are described. The improved RL with Q-learning algorithm is implemented to acquire the optimal control strategies for improving fuel economy. The Markov Chain is employed to calculate the Transition Probability Matrix of the required power. A Kullback-Leibler (KL) divergence rate is designed to activate the update of EMS, when a new corresponding driving cycle is expected. An Exploration Factor (FT) is proposed to overcome the disadvantages of the normal RL algorithm in convergence rate and reward cost evaluation. The diverse KL divergence rates are examined to seek optimal solutions. The normal-RI, strategy, rule-based strategy, and dynamic programming strategy are implemented as benchmark strategies to verify the effectiveness of the proposed strategy. The validation results indicate that the improved RL algorithm with FY makes it possible to promote the EMS capable of significantly improving the energy efficiency of a plug-in PHEV.

关键词： Hybrid electric vehicle energy management strategy reinforcement learning algorithm self-adaptive exploration factor

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adaptive Data Replication Optimization Based on reinforcement learning

Adaptive Data Replication Optimization Based on Reinforcemen...

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ieee symposium Series on Computational Intelligence (ieee SSCI)

作者： Wee, Chee Keong Nayak, Richi eHlth Queensland Business Applicat Technol Serv Digital Applicat Serv Brisbane Qld Australia Queensland Univ Technol Sci & Engn Fac Sch Elect Engn & Comp Sci Brisbane Qld Australia

ISBN: (纸本)9781728125473

Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software's configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software's configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.

关键词： Data replication optimization meta-heuristics deep reinforcement learning

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An Online reinforcement learning Wing-Tracking Mechanism for Flexible Wing Aircraft 13

An Online Reinforcement Learning Wing-Tracking Mechanism for...

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13th ieee International symposium on Robotic and Sensors Environments (ROSE)

作者： Abouheaf, Mohammed Mailhot, Nathaniel Gueaieb, Wail Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Aswan Univ Coll Energy Engn Aswan Egypt Univ Ottawa Dept Mech Engn Ottawa ON Canada

ISBN: (纸本)9781728119649

Flexible wing aircraft are gaining an increasing interest due to their salient features, such as inexpensive market price, low-cost operation, in-flight robustness, multi-purpose use, and their ability to operate with very little infrastructure. The continuous variations in the aerodynamics of the wing and additionally the kinematic and dynamic constraints that evolve due to the wing-fuselage interactions make the modeling task of such systems ultimately challenging. An online model-free adaptive control mechanism based on two linear actuation systems is proposed in this manuscript to fulfill different pitch-roll maneuvers. The mechanism employs model-free tracking control strategies and utilizes a real-time value iteration-based reinforcement learning process. The adaptation of the control gains is accomplished online using means of adaptive critics.

关键词： Mathematical model Adaptation models Aerodynamics Aircraft Atmospheric modeling adaptive learning reinforcement learning

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