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检索条件"任意字段=IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning"

共 307 条记录，以下是241-250 订阅

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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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Proceedings - ieee 14th international symposium on Parallel and Distributed Computing, ISPDC 2015

Proceedings - IEEE 14th International Symposium on Parallel ...

引用

14th ieee international symposium on Parallel and Distributed Computing, ISPDC 2015

ISBN: (纸本)9781467371483

The proceedings contain 26 papers. The topics discussed include: getting ready for approximate computing: trading parallelism for accuracy for DSS workloads;dataClay: the integration of persistent data, parallel programming models, and true sharing;Intel architecture and technology for future HPC system building blocks;personalized motion sensor driven gesture recognition in the FIWARE cloud platform;a simulator for analysis of opportunistic routing algorithms;multilevel task parallelism exploitation on asymmetric sets of tasks and when using third-party tools;cache affinity optimization techniques for scaling software transactional memory systems on multi-CMP architectures;high-speed security analytics powered by in-memory machine learning engine;GPU-accelerated digital halftoning by the local exhaustive search;analyzing memory access on CPU-GPGPU shared LLC architecture;and schedule dynamic multiple parallel jobs with precedence-constrained tasks on heterogeneous distributed computing systems.

关键词： Memory architecture

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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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Multi-agent Deep reinforcement learning based Information-Energy Collaboration in Vehicle Edge Computing Networks 35

Multi-agent Deep Reinforcement Learning based Information-En...

引用

35th ieee international symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Feng, Yaoyu Zhang, Biling Yu, Jung-Lang Beijing University of Posts and Telecommunications School of Network Education China Fu Jen Catholic University Department of Electrical Engineering New Taipei City24205 Taiwan

ISBN: (纸本)9798350362244

In the vehicle edge computing network (VECN), how to deal with the computation resources and energy resources shortage problem the roadside units (RSUs) encounter when they are performing delay sensitive computation tasks is an important issue, especially during the peak hours and the situation of VECN is dynamic. To complete the computation tasks on time with the minimum expenditure, in this paper, we investigate the problem of information-energy collaboration among RSUs, where the spectrum management is also involved. For the considered scenario, the RSUs' strategies of spectrum selection, computation task offloading and energy sharing are derived from the formulated optimization problem. Since the proposed problem is a highly complex mixed-integer nonlinear programming problem and the strategies are coupled with each other, a multi-agent deep deterministic policy gradient (MADDPG) based algorithm is proposed to find the sub-optimal solutions quickly in a dynamic environment. The simulation results show that our approach is superior to the existing schemes in terms of total system expenditure and the spectral efficiency. © 2024 ieee.

关键词： Computation offloading

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Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device

Using approximate dynamic programming for estimating the rev...

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Vincent François-Lavet Raphael Fonteneau Damien Ernst Department of Electrical Engineering and Computer Science University of Liège Belgium

This paper proposes a methodology to estimate the maximum revenue that can be generated by a company that operates a high-capacity storage device to buy or sell electricity on the day-ahead electricity market. The methodology exploits the dynamic programming (DP) principle and is specified for hydrogen-based storage devices that use electrolysis to produce hydrogen and fuel cells to generate electricity from hydrogen. Experimental results are generated using historical data of energy prices on the Belgian market. They show how the storage capacity and other parameters of the storage device influence the optimal revenue. The main conclusion drawn from the experiments is that it may be advisable to invest in large storage tanks to exploit the inter-seasonal price fluctuations of electricity.

关键词： Electricity Hydrogen Fuel cells Electrochemical processes Hydrogen storage dynamic programming

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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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Iterative local dynamic programming

Iterative local dynamic programming

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Emanuel Todorov Yuval Tassa Department of Cognitive Science University of California San Diego USA Center of Neural Computation Hebrew University of Jerusalem Israel

We develop an iterative local dynamic programming method (iLDP) applicable to stochastic optimal control problems in continuous high-dimensional state and action spaces. Such problems are common in the control of biological movement, but cannot be handled by existing methods. iLDP can be considered a generalization of differential dynamic programming, in as much as: (a) we use general basis functions rather than quadratics to approximate the optimal value function; (b) we introduce a collocation method that dispenses with explicit differentiation of the cost and dynamics and ties iLDP to the unscented Kalman filter; (c) we adapt the local function approximator to the propagated state covariance, thus increasing accuracy at more likely states. Convergence is similar to quasi-Newton methods. We illustrate iLDP on several problems including the ldquoswimmerrdquo dynamical system which has 14 state and 4 control variables.

关键词： dynamic programming Function approximation Optimal control Open loop systems Costs Iterative methods Stochastic processes Control systems Stochastic resonance learning

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approximate real-time optimal control based on sparse Gaussian process models

Approximate real-time optimal control based on sparse Gaussi...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Joschka Boedecker Jost Tobias Springenberg Jan Wülfing Martin Riedmiller Department of Computer Science University of Freiburg Freiburg Germany

In this paper we present a fully automated approach to (approximate) optimal control of non-linear systems. Our algorithm jointly learns a non-parametric model of the system dynamics - based on Gaussian Process Regression (GPR) - and performs receding horizon control using an adapted iterative LQR formulation. This results in an extremely data-efficient learning algorithm that can operate under real-time constraints. When combined with an exploration strategy based on GPR variance, our algorithm successfully learns to control two benchmark problems in simulation (two-link manipulator, cart-pole) as well as to swing-up and balance a real cart-pole system. For all considered problems learning from scratch, that is without prior knowledge provided by an expert, succeeds in less than 10 episodes of interaction with the system.

关键词： Approximation methods Trajectory Computational modeling Optimization Predictive models Approximation algorithms Optimal control

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An integrated design for intensified direct heuristic dynamic programming

An integrated design for intensified direct heuristic dynami...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Xiong Luo Jennie Si Yuchao Zhou School of Computer and Communication Engineering University of Science and Technology Beijing (USTB) Beijing China Arizona State University Tempe AZ US

There has been a growing interest in the study of adaptive/approximate dynamic programming (ADP) in recent years. The ADP technique provides a powerful tool to understand and improve the principled technologies of machine intelligence system. As one of the ADP algorithms based on adaptive critic neural networks (NNs), the direct heuristic dynamic programming (direct HDP) has demonstrated some successful applications in solving realistic engineering control problems. In this study, based on a three-network architecture in which the reinforcement signal is approximated by an additional NN, a novel integrated design method for intensified direct HDP is developed. The new design approach is implemented by using multiple PID neural networks (PIDNNs), which effectively takes into account structural knowledge of system states and control that are usually present in a physical system. By using a Lyapunov stability approach, a uniformly ultimately boundedness (UUB) result is proved for our PIDNNs-based intensified direct HDP learning controller. Furthermore, the learning and control performances of the proposed design is tested using the popular cart-pole example to illustrate the key ideas of this paper.

关键词： Neural networks dynamic programming Convergence Lyapunov methods learning (artificial intelligence) Educational institutions Algorithm design and analysis

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An approximate dynamic programming based controller for an underactuated 6DoF quadrotor

An approximate Dynamic Programming based controller for an u...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Emanuel Stingu Frank L. Lewis Automation & Robotics Research Institute University of Texas Arlington Arlington TX USA

This paper discusses how the principles of Adaptive dynamic programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning using an actor-critic structure. Due to the complexity of the quadrotor system, the learning process has to use as much information as possible about the system and the environment. Various methods to improve the learning speed and efficiency are presented. Neural networks with local activation functions are used as function approximators because the state-space can not be explored efficiently due to its size and the limited time available. The complex dynamics is controlled by a single critic and by multiple actors thus avoiding the curse of dimensionality. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function.

关键词： Equations Optimal control Rotors Heuristic algorithms Neurons Artificial neural networks Approximation methods

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