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7th Asian Control Conference (ASCC 2009)

作者： Lewis, Frank L. Vrabie, Draguna Univ Texas Arlington Automat & Robot Res Inst Ft Worth TX 76118 USA

ISBN: (纸本)9781424454402

Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior. Relations are show between ADP and adaptive control.

关键词： reinforcement learning

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adaptive Fuzzy Optimal Control for Interconnected Switched Nonlinear Systems With Dwell Time

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ieee TRANSACTIONS ON FUZZY SYSTEMS 2024年第6期32卷 3319-3328页

作者： Zheng, Licheng Liu, Junhe Chen, C. L. Philip Zhang, Yun Chen, Ci Wu, Zongze Liu, Zhi Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Hong Kong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

This article focuses on the problem of adaptive optimal tracking control of interconnected switched systems on dwell time. The objective is to establish interconnection between switched systems, stabilize each switched system, and achieve optimal performance over the dwell time. An actor-critic learning technique is utilized to design the optimal controller by evaluating the performance via the critic and improving the policy via the actor. During the design of the cost function and optimization process, we employed nonzero-sum game theory to incorporate the interconnection factors among systems. The proposed control scheme designs a novel relaxed stability condition for switched subsystems and validates the effectiveness by using a Lyapunov function with a value function. Finally, the designed control scheme can be verified by simulation.

关键词： Switches Switched systems Control systems Stability analysis Interconnected systems Optimal control reinforcement learning Actor-critic learning adaptive dynamic programming adaptive fuzzy control dwell time interconnected switched systems

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Collaborative Optimization of Energy Management Strategy and adaptive Cruise Control Based on Deep reinforcement learning

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ieee TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2023年第1期9卷 34-44页

作者： Peng, Jiankun Fan, Yi Yi, Guodong Jiang, Ruhai Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China CAS Juche Technol Co Ltd Hefei 230091 Peoples R China

Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption, and adaptive cruise control (ACC) technology provides safe and convenient travel for drivers. The fusion of the two technologies can theoretically improve the safety, comfort, and fuel economy of vehicles. Hence, the energy management strategy (EMS) of Prius, a typical HEV configuration, is studied under the car-following scenario. This optimization problem involves complex systems, inconsistent objectives, and stringent constraints, which may be challenging to conventional algorithms. Therefore, a novel deep deterministic policy gradient (DDPG)-based ecological driving strategy (DDPG-ECO) is proposed and the weights of multiple objectives are analyzed to optimize the training results. The extensive simulation experiment compares the effects of Ornstein-Uhlenbeck action noise (OUAN) and soft-max action noise (SAN), which act on the acceleration action. Simulations under different driving cycles show that the fuel economy of DDPG-ECO can achieve more than 90% of dynamic programming (DP)-based methods on the conditions of ensuring car-following performances.

关键词： adaptive cruise control (ACC) deep reinforcement learning (DRL) energy management strategy (EMS) multiobjective optimization problem

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adaptive critic-based neurofuzzy controller for the steam generator water level

Adaptive critic-based neurofuzzy controller for the steam ge...

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15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors/ 2006 ieee Nuclear Science symposium

作者： Fakhrazari, Amin Boroushaki, Mehrdad Sharif Univ Technol Dept Mech Engn Tehran Iran

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

关键词： adaptive critic-based design fuzzy logic reinforcement learning vertical U-tube steam generator

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adaptive Slope Locomotion with Deep reinforcement learning

Adaptive Slope Locomotion with Deep Reinforcement Learning

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ieee/SICE International symposium on System Integration (SII)

作者： Jones, William Blum, Tamir Yoshida, Kazuya Tohoku Univ Grad Sch Engn Dept Aerosp Engn Space Robot Lab Sendai Miyagi 9808579 Japan

ISBN: (纸本)9781728166674

In this paper we present a model free Deep reinforcement learning based approach to the motion planning problem of a quadruped moving from a flat to an inclined plane. In our implementation, we do not provide any prior information of the location of the inclined plane, nor pass any vision data during the training process. With this approach, we train a 12 degree of freedom quadruped robot to traverse up and down a variety of simulated sloped environments, in the process demonstrating that deep reinforcement learning is able to generate highly dynamic and adaptable solutions.

关键词： reinforcement learning Quadruped Slope Walking DPPO V-REP

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A theoretical analysis of cooperative behaviorin multi-agent Q-learning

A theoretical analysis of cooperative behaviorin multi-agent...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Waltman, Ludo Kaymak, Uzay Erasmus Univ Erasmus Sch Econ POB 1738 NL-3000 DR Rotterdam Netherlands

ISBN: (纸本)9781424407064

A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Qlearning. In some studies cooperative behavior did emerge, in others it did not. This paper provides a theoretical analysis of this issue. The analysis focuses on multi-agent Q-learning in iterated prisoner's dilemmas. It is shown that under certain assumptions cooperative behavior may emerge when multi-agent Q-learning is applied in an iterated prisoner's dilemma. An important consequence of the analysis is that multi-agent Q-learning may result in non-Nash behavior. It is found experimentally that the theoretical results presented in this paper are quite robust to violations of the underlying assumptions.

关键词： Multi agent systems

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Hamiltonian-Driven adaptive dynamic programming With Approximation Errors

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ieee TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13762-13773页

作者： Yang, Yongliang Modares, Hamidreza Vamvoudakis, Kyriakos G. He, Wei Xu, Cheng-Zhong Wunsch, Donald C. Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Michigan State Univ Mech Engn Dept E Lansing MI 48824 USA Georgia Tech Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA Univ Macau Fac Sci & Technol State Key Lab Internet Things Smart City Macau Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

In this article, we consider an iterative adaptive dynamic programming (ADP) algorithm within the Hamiltonian-driven framework to solve the Hamilton-Jacobi-Bellman (HJB) equation for the infinite-horizon optimal control problem in continuous time for nonlinear systems. First, a novel function, ``min-Hamiltonian,'' is defined to capture the fundamental properties of the classical Hamiltonian. It is shown that both the HJB equation and the policy iteration (PI) algorithm can be formulated in terms of the min-Hamiltonian within the Hamiltonian-driven framework. Moreover, we develop an iterative ADP algorithm that takes into consideration the approximation errors during the policy evaluation step. We then derive a sufficient condition on the iterative value gradient to guarantee closed-loop stability of the equilibrium point as well as convergence to the optimal value. A model-free extension based on an off-policy reinforcement learning (RL) technique is also provided. Finally, numerical results illustrate the efficacy of the proposed framework.

关键词： Costs Mathematical model Stability analysis Approximation error Approximation algorithms dynamic programming Iterative algorithms Hamilton-Jacobi-Bellman (HJB) equation Hamiltonian-driven framework inexact adaptive dynamic programming (ADP) optimal control

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adaptive Optimal Control for Discrete-Time Linear Systems via Hybrid Iteration 12

Adaptive Optimal Control for Discrete-Time Linear Systems vi...

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ieee 12th Data Driven Control and learning Systems Conference (DDCLS)

作者： Qasem, Omar Gao, Weinan Gutierrez, Hector Florida Inst Technol Coll Engn & Sci Melbourne FL 32901 USA Northeastern Univ State Key Lab Integrated Automat Proc Ind Shenyang 110004 Liaoning Peoples R China

ISBN: (纸本)9798350321050

In this paper, a novel dynamic programming (DP) and adaptive dynamic programming (ADP) algorithms are proposed, namely hybrid iteration (HI), for discrete-time linear systems. The proposed HI approach fill up the performance gap of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of the prior knowledge of an initial stabilizing control policy required in PI, and at the same time it maintains a fast quadratic convergence rate compared with VI. A data-driven adaptive optimal controller design is also proposed based on the proposed HI algorithm. Simulation results for randomly generated discrete-time linear systems with different system orders demonstrate that the proposed HI approach can significantly save CPU time and reduce the number of learning iterations to converge to the optimal solution comparing with the VI approach. The data-driven HI method is implemented to an application of turbocharged diesel engine with exhaust gas recirculation, and the simulation results illustrate the efficacy of the proposed HI method.

关键词： Hybrid iteration discrete-time systems dynamic programming (DP) adaptive dynamic programming (ADP) adaptive optimal control reinforcement learning

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

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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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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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