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Chinese Automation Congress (CAC)

作者： Liu, Yang Xing, Zuoxia Zhao, Lijun Li, Li Xu, Jian Fu, Qitong Shenyang Univ Technol Sch Elect Engn Shenyang Peoples R China BMW Brilliance Automot Ltd Technol Steering & Maintenance Shenyang Peoples R China

ISBN: (纸本)9781728176871

In this paper, an data-based H-infinity tracking control algorithm is studied for the discrete delay system with disturbances. The augmented system is constructed and then transformed into a data-based system consist of inputs, outputs, and reference trajectories, which replace the state vector by a data vector. Next, based on the Bellman optimality principle, the historical data is adopted to derive a new Bellman equation. In order to realize H-infinity tracking control, the input and disturbance are generalized to the zero-sum game problem. Finally, by collecting operational data online, a H-infinity tracking control method based on data-based reinforcement learning algorithm is presented. Simulation example shows that the proposed reinforcement learning tracking control method is effective.

关键词： ADP Data-based Discrete system H-infinity control

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adaptive Fuzzy Control of Switched Objective Functions in Pursuit-Evasion Scenarios 49

Adaptive Fuzzy Control of Switched Objective Functions in Pu...

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49th ieee Conference on Decision and Control (CDC)

作者： Goode, Brian Kurdila, Andrew Roan, Mike Virginia Polytech Inst & State Univ Dept Mech Engn Blacksburg VA 24060 USA

ISBN: (纸本)9781424477463

In recent efforts, the authors have derived simple switched control schemes that qualitatively yield an attractive performance in two player pursuit-evasion games. A drawback of these methods is that detailed knowledge of an opponent's dynamics and strategy is required to implement the switching controller. Furthermore, an objective evaluated over a finite horizon may not guide an agent to the target set. To circumvent this potential shortcoming, a switching scheme is proposed where an adaptive fuzzy controller chooses the best objective function from a predefined library to increase the agent's reachability. The methodology we present builds on the common approximate dynamic programming reinforcement learning technique. We give conditions for showing when the controller is applicable and give an implementation example with the Homicidal Chauffeur problem.

关键词： reinforcement learning

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Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online learning Optimal Control Approach

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2014年第2期25卷 418-428页

作者： Liu, Derong Wang, Ding Li, Hongliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.

关键词： adaptive dynamic programming decentralized control large-scale systems neural networks nonlinear interconnected systems optimal control policy iteration reinforcement learning

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adaptive dynamic programming for Cooperative Control with Incomplete Information

Adaptive Dynamic Programming for Cooperative Control with In...

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ieee International Conference on Systems, Man, and Cybernetics (SMC)

作者： Koepf, Florian Ebbert, Sebastian Flad, Michael Hohmann, Soeren Karlsruhe Inst Technol Inst Control Syst IRS Karlsruhe Germany

ISBN: (纸本)9781538666500

There is a trend towards interconnected and complex dynamical systems that are controlled by more than one controller. Due to the coupling of the controllers by means of the system, these interacting controllers need to consider not only the system dynamics but also the influence of each other. However, in realistic scenarios, they usually do not exchange all the information concerning their parameters and control laws and an exact model of the system dynamics is often hard to obtain. This is why we consider the challenging setting where the controllers have no access neither to the parameters of each other nor to the system dynamics. The controller design is quite difficult in this scenario, as the final system configuration is not known during the design process. In this complex scenario, we propose algorithms where each controller uses adaptive dynamic programming to adapt its control law. Here, each controller strives for reaching its individual control objectives, a setting which can be formulated as a coupled optimization problem, respectively a dynamic game. As an example, we consider a vehicle model with two lateral controllers. With our proposed algorithms, the controllers converge successfully to a solution of the coupled optimization problem without knowing the parameters of each other and the system dynamics.

关键词： Cooperative Control adaptive dynamic programming adaptive Optimal Control Game Theory reinforcement learning

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adaptive Optimal Control via Continuous-Time Q-learning for Unknown Nonlinear Affine Systems 58

Adaptive Optimal Control via Continuous-Time Q-Learning for ...

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58th ieee Conference on Decision and Control (CDC)

作者： Chen, Anthony Siming Herrmann, Guido Univ Bristol Dept Mech Engn Bristol BS8 1QU Avon England Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113982

This paper proposes two novel adaptive optimal control algorithms for continuous-time nonlinear affine systems based on reinforcement learning: i) generalised policy iteration (GPI) and ii) Q-learning. As a result, the a priori knowledge of the system drift f(x) is not needed via GPI, which gives us a partially model-free and online solution. We then for the first time extend the idea of Q-learning to the nonlinear continuous time optimal control problem in a noniterative manner. This leads to a completely model-free method where neither the system drift f(x) nor the input gain g(x) is needed. For both methods, the adaptive critic and actor are continuously and simultaneously updating each other without iterative steps, which effectively avoids the hybrid structure and the need for an initial stabilising control policy. Moreover, finite-time convergence is guaranteed by using a sliding mode technique in the new adaptive approach, where the persistent excitation (PE) condition can be directly verified online. We also prove the overall Lyapunov stability and demonstrate the effectiveness of the proposed algorithms using numerical examples.

关键词： adaptive optimal control Q-learning non-linear systems reinforcement learning approximate dynamic programming adaptive critic

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A study of dynamically adjustment for exploitation action using evaluation of achievement 28

A study of dynamically adjustment for exploitation action us...

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28th Int symposium on Micro-NanoMechatronics and Human Science (MHS) - From Micro and Nano Scale Systems to Robotics and Mechatronics Systems / symposium on Understanding Brain Plasticity on Body Representations to Promote Their adaptive Functions

作者： Sugimoto, Masashi Kurashige, Kentarou Kagawa Coll Natl Inst Technol 551 Kohda Mitoya Kagawa 7691192 Japan Muroran Inst Technol 27-1 Mizumoto Muroran Hokkaido 0508585 Japan

ISBN: (纸本)9781538633151

Over the years, many studies have been conducted with the objective of facilitating the working of robots in dynamic environments. Various robots have been developed to assist humans in workspaces, such as a house or factory. In general, robots are required to work effectively and safely in a dynamic environment to achieve their tasks. However, it is not easy to make a robot behave like a human in dynamic environments. When they are working in a certain environment, humans select an appropriate course of action through subconsciously predicting all the changes in the environment and their next state. For achievement these problems, recent years, various machine learning methods have been suggested. In reinforcement learning, it attracts attention as the technique that often use in actual robot. However, reinforcement learning has some problems. In one of the problems, robot does not copes with changing purpose in reinforcement learning. reinforcement learning has been demanded to achieve various purpose, because what request to robot is diversifying and to achieve various purpose in robot have been wanted, as mentioned above. Therefore, It is important to solve this problem.

关键词： Robots Task analysis learning (artificial intelligence) Atmosphere Electronic mail Production facilities learning systems

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reinforcement learning Control of Power Systems with Unknown Network Model under Ambient and Forced Oscillations 4

Reinforcement Learning Control of Power Systems with Unknown...

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4th ieee Conference on Control Technology and Applications (ieee CCTA)

作者： Mukherjee, Sayak Bai, He Chakrabortty, Aranya North Carolina State Univ Dept ECE Raleigh NC 27695 USA Oklahoma State Univ Sch Mech & Aerosp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781728171401

We present a model-free optimal control design for electric power systems with unknown transmission network and load models to improve its dynamic performance using techniques from reinforcement learning (RL) and adaptive dynamic programming (ADP). We consider different persistent disturbances in the grid including ambient oscillations resulting from load fluctuations and their effects on exciter voltage regulation loops. We also consider forced oscillation scenarios that frequently occur due to malfunctioning of governor valves. Our proposed RL algorithm recovers the optimal feedback response in spite of all of these disturbances in a completely model-free way using online measurements of the states, inputs, and the disturbances. The design is validated using the ieee benchmark 39-bus, 10-generator New England power system model perturbed with different ambient and forced oscillations.

关键词： reinforcement learning (RL) power systems ambient oscillations forced oscillations model-free control

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RACE: A reinforcement learning Framework for Improved adaptive Control of NoC Channel Buffers 22

RACE: A Reinforcement Learning Framework for Improved Adapti...

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32nd Great Lakes symposium on VLSI (GLSVLSI)

作者： Khan, Kamil Pasricha, Sudeep Kim, Ryan Gary Colorado State Univ Elect & Comp Engn Ft Collins CO 80523 USA

ISBN: (纸本)9781450393225

Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching. Recently, reversible multi-function channel (RMC) buffers have been proposed to simultaneously reduce power and enable adaptive NoC buffering between adjacent routers. While adaptive buffering can improve NoC performance by maximizing buffer utilization, controlling the RMC buffer allocations requires a congestion-aware, scalable, and proactive policy. In this work, we present RACE, a novel reinforcement learning (RL) framework that utilizes better awareness of network congestion and a new reward metric ("falsefulls") to help guide the RL agent towards better RMC buffer control decisions. We show that RACE reduces NoC latency by up to 48.9%, and energy consumption by up to 47.1% against state-of-the-art NoC buffer control policies.

关键词： Network-on-chip machine learning dynamic buffering

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Optimal Control for a Class of Unknown Nonlinear Systems via the Iterative GDHP Algorithm

Optimal Control for a Class of Unknown Nonlinear Systems via...

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8th International symposium on Neural Networks

作者： Wang, Ding Liu, Derong Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783642210891

Using the neural-network-based iterative adaptive dynamic programming (ADP) algorithm, an optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is proposed in this paper. The optimal controller is designed with convergence analysis in terms of cost function and control law. In order to implement the algorithm via globalized dual heuristic programming (CDHP) technique, a neural network is constructed first to identify the unknown nonlinear system, and then two other neural networks are used to approximate the cost function and the control law, respectively. An example is provided to verify the effectiveness of the present approach.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming intelligent control neural networks optimal control reinforcement learning

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adaptive robust control of the continuous-time two-input systems with unknown disturbance based on Q-function 12

Adaptive robust control of the continuous-time two-input sys...

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

作者： Lv, Yongfeng Cui, Zhengyu Wang, Minlin Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China

ISBN: (纸本)9798350321050

Considering overshoot and chatter of the multi-input system with unknown interference, this paper studies the adaptive robust optimal controls of continuous-time two-input systems with an approximate dynamic programming (ADP) based Q-function scheme. A complex Hamilton-Jacobi-Issacs (HJI) equation is obtained with the two-input system and the zero-game theory, where a value function is constructed. Solving the HJI equation is a challenging task. Thus, an ADP-based Q-function with a neural network is constructed to learn the saddle point of the HJI equation. Simultaneously, an integral reinforcement signal of the critic networks is introduced such that the system drift and input dynamics in the HJI equation are relaxed when studying the saddle-point intractable solution. Then, the adaptive robust optimal actor and worst disturbance are approximated with another three networks. Finally, an F-16 aircraft plant is used to verify the proposed ADP-based Q-function.

关键词： Robust control adaptive control approximate dynamic programming multi-input system

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