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检索条件"任意字段=2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2009"

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adaptive dynamic programming-based optimal tracking control for nonlinear systems using general value iteration

Adaptive dynamic programming-based optimal tracking control ...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Xiaofeng Lin Qiang Ding Weikai Kong Chunning Song Qingbao Huang School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9781479945511

For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptive dynamic programming is proposed in this paper. By system transformation, the optimal tracking problem is converted into the optimal regulating problem for the tracking error dynamics. Then, general value iteration algorithm is developed to obtain the optimal control with convergence analysis. Considering the advantages of echo state network, we use three echo state networks with levenberg-Marquardt (LM) adjusting algorithm to approximate the system, the cost function and the control law. A simulation example is given to demonstrate the effectiveness of the presented scheme.

关键词： Cost function Nonlinear systems Optimal control Trajectory dynamic programming Approximation algorithms

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Optimal control for a class of nonlinear systems with state delay based on adaptive dynamic programming with ε-error bound

Optimal control for a class of nonlinear systems with state ...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Xiaofeng Lin Nuyun Cao Yuzhang Lin School of Electrical Engineering Guangxi University Nanning China Department of Electrical Engineering Tsinghua University Beijing China

In this paper, a finite-horizon ε-optimal control for a class of nonlinear systems with state delay is proposed by adaptive dynamic programming (ADP) algorithm. First of all, the performance index function is defined and the Hamilton-Jacobi-Bellman (HJB) equation is obtained for the problem, the convergence of the iterative algorithm is also presented. Then, ADP algorithm for finite-horizon optimal control is introduced with an ε-error bound so as to get the ε-optimal control, and BP neural network is used to implement ADP algorithm. At last, an example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： Performance analysis Optimal control Delays Heuristic algorithms Nonlinear systems dynamic programming Neural networks

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ADP-based optimal control for a class of nonlinear discrete-time systems with inequality constraints

ADP-based optimal control for a class of nonlinear discrete-...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Yanhong Luo Geyang Xiao College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479945511

In this paper, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of nonlinear discrete-time (DT) systems with inequality constraints. To begin with, the initial constrained optimal control problem is transformed into an infinite horizon optimal control problem by introducing the penalty function. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with two neural networks. The two neural networks are aimed at generating the optimal cost and the optimal control policy respectively. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

关键词： Optimal control Biological neural networks Nonlinear systems dynamic programming Cost function

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Feedback controller parameterizations for reinforcement learning

Feedback controller parameterizations for Reinforcement Lear...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： John W. Roberts Ian R. Manchester Russ Tedrake MIT CSAIL Cambridge MA USA

reinforcement learning offers a very general framework for learning controllers, but its effectiveness is closely tied to the controller parameterization used. Especially when learning feedback controllers for weakly stable systems, ineffective parameterizations can result in unstable controllers and poor performance both in terms of learning convergence and in the cost of the resulting policy. In this paper we explore four linear controller parameterizations in the context of REINFORCE, applying them to the control of a reaching task with a linearized flexible manipulator. We find that some natural but naive parameterizations perform very poorly, while the Youla Parameterization (a popular parameterization from the controls literature) offers a number of robustness and performance advantages.

关键词： adaptive control Torque Observers Trajectory Transfer functions Linear systems Kalman filters

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adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

Adaptive dynamic programming for optimal control of unknown ...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Derong Liu Ding Wang Dongbin Zhao Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach.

关键词： Artificial neural networks Riccati equations Integrated optics Optimal control Neurons

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Bayesian active learning with basis functions

Bayesian active learning with basis functions

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Ilya O. Ryzhov Warren B. Powell Operations Research and Financial Engineering Princeton University Princeton NJ USA

A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may lead to poor decisions, making it necessary to sometimes explore actions that appear to be suboptimal. We propose a Bayesian strategy for active learning with basis functions, based on the knowledge gradient concept from the optimal learning literature. The new method performs well in numerical experiments conducted on an energy storage problem.

关键词： Tin Function approximation Bayesian methods Covariance matrix Mathematical model dynamic programming

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Supervised adaptive dynamic programming based adaptive cruise control

Supervised adaptive dynamic programming based adaptive cruis...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Dongbin Zhao Zhaohui Hu Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China

This paper proposes a supervised adaptive dynamic programming (SADP) algorithm for the full range adaptive cruise control (ACC) system. The full range ACC system considers both the ACC situation in highway system and the stop and go (SG) situation in urban street way system. It can autonomously drive the host vehicle with desired speed and distance to the preceding vehicle in both situations. A traditional adaptive dynamic programming (ADP) algorithm is suited for this problem, but it suffers from the low learning efficiency. We propose the concept of inducing range to construct the supervisor and finally formulate the SADP algorithm, which greatly speeds up the learning efficiency. Several driving scenarios are designed and tested with the trained controller compared to traditional ones by simulation results, showing that trained SADP performs very well in all the scenarios, so that it provides an effective approach for the full range ACC problem.

关键词： Vehicles Acceleration Driver circuits Training Control systems Artificial neural networks Real time systems

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N-step optimal time-invariant trajectory tracking control for a class of nonlinear systems

N-step optimal time-invariant trajectory tracking control fo...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Ruizhuo Song Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang China

In this paper, the time-invariant trajectory tracking control problem under N-step control is solved by finite horizon approximate dynamic programming (ADP) algorithms. At first, we convert the tracking control problem for time-invariant trajectory into a output regulation problem. The cost function guarantees the energy is minimum. Secondly, the regulation control scheme is proposed using finite horizon ADP technique to obtain the N-step control. Then two theorems are used to prove the convergence of the proposed control algorithm. Finally, the simulation is given to demonstrate the effectiveness and feasibility of the control scheme.

关键词： Trajectory Cost function Nonlinear systems Optimal control dynamic programming Convergence Mathematical model

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Kalman Temporal Differences: The deterministic case

Kalman Temporal Differences: The deterministic case

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： Matthieu Geist Olivier Pietquin Gabriel Fricout IMS Research Group Supélec Metz France IMS Research Group Metz France MC cluster ArcelorMittal Research Maizieres-Les-Metz France

This paper deals with value function and Q-function approximation in deterministic Markovian decision processes. A general statistical framework based on the Kalman filtering paradigm is introduced. Its principle is to adopt a parametric representation of the value function, to model the associated parameter vector as a random variable and to minimize the mean-squared error of the parameters conditioned on past observed transitions. From this general framework, which will be called Kalman Temporal Differences (KTD), and using an approximation scheme called the unscented transform, a family of algorithms is derived, namely KTD-V, KTD-SARSA and KTD-Q, which aim respectively at estimating the value function of a given policy, the Q-function of a given policy and the optimal Q-function. The proposed approach holds for linear and nonlinear parameterization. This framework is discussed and potential advantages and shortcomings are highlighted.

关键词： Kalman filters Equations State-space methods learning Error correction Filtering Random variables Approximation algorithms dynamic programming Vectors

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A reinforcement learning approach for sequential mastery testing

A reinforcement learning approach for sequential mastery tes...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)

作者： El-Sayed M. El-Alfy College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.

关键词： Testing Bayesian methods learning Accuracy Decision theory Measurement Adaptation models

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