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Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method

NEUROCOMPUTING 2019年 340卷 158-170页

作者： Yang, Dongsheng Li, Ting Zhang, Huaguang Xie, Xiangpeng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China

In this paper, an online integral reinforcement learning strategy is proposed to deal with robust constrained control problems using event-triggered mechanism for nonlinear Continuous-Time (C-T) systems with external disturbances. The novel design of constrained control law is addressed together with the adaptive event-triggered condition by guaranteeing the optimal performance and system stability. An adaptive online actor-critic Neural Network (NN) reinforcement learning scheme is developed to approximate the optimal solution of the complicated Hamilton-Jacobi-Isaacs equation. Meanwhile, the convergence of NN weight errors and the event-triggered closed-loop system stability are demonstrated to be uniform ultimate bounded by Lyapunov analysis under the proposed triggering condition. Moreover, event-triggered H-infinity tracking control with input constrains and limited network communication is also presented by establishing an augmented system. Finally, simulation results are provided to show the algorithm validity. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Event-triggered control Robust H-infinity control Hamilton-Jacobi-Isaacs (HJI) equation Neural networks Input constrains

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An Adaptive dynamic programming Algorithm to Solve Optimal Control of Uncertain Nonlinear Systems

An Adaptive Dynamic Programming Algorithm to Solve Optimal C...

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ieee symposium on Adaptive dynamic programming and reinforcement learning (adprl)

作者： Cui, Xiaohong Luo, Yanhong Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781479945528

In this paper, an approximate optimal control method based on adaptive dynamic programming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neural network systems, where the critic -action networks approximate the optimal value function and optimal control and the other two neural networks approximates the unknown system. Furthermore the adaptive tuning laws are given based on Lyapunov approach, which ensures the uniform ultimate bounded stability of the closed-loop system. Finally, the effectiveness is demonstrated by a simulation example.

关键词： Closed loop systems

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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 ...

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ieee symposium on Adaptive dynamic programming and reinforcement learning (adprl)

作者： Lin, Xiaofeng Ding, Qiang Kong, Weikai Song, Chunning Huang, Qingbao Guangxi Univ Sch Elect Engn Nanning Peoples R China

ISBN: (纸本)9781479945528

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： Adaptive dynamic programming value iteration tracking control echo state network

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A Comparison of approximate dynamic programming Techniques on Benchmark Energy Storage Problems: Does Anything Work?

A Comparison of Approximate Dynamic Programming Techniques o...

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ieee symposium on Adaptive dynamic programming and reinforcement learning (adprl)

作者： Jiang, Daniel R. Pham, Thuy V. Powell, Warren B. Salas, Daniel F. Scott, Warren R.

ISBN: (纸本)9781479945528

As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).

关键词： dynamic programming energy storage power engineering computing power system management renewable energy sources table lookup ADP API AVI approximate dynamic programming approximate policy iteration approximate value iteration backward induction dynamic programming techniques energy storage control policy lookup table natural solution technique solar energy stochastic dynamic programs wind energy Approximation algorithms Benchmark testing Energy storage Equations Function approximation Mathematical model Table lookup dynamic programming energy storage Power system management AVI Benchmark testing Power engineering computing function approximation Approximation algorithms Adenosine Diphosphate Automatic data processing Renewable energy renewable energy sources Wind energy Solar Energy

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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...

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ieee symposium on Adaptive dynamic programming and reinforcement learning (adprl)

作者： Francois-Lavet, Vincent Fonteneau, Raphael Ernst, Damien Univ Liege Dept Elect Engn & Comp Sci B-4000 Liege Belgium

ISBN: (纸本)9781479945528

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： dynamic programming electrolysis fuel cells hydrogen storage power markets Belgian market day-ahead electricity market dynamic programming principle high-capacity storage device hydrogen-based storage devices interseasonal price fluctuations maximum revenue estimation optimal revenue dynamic programming Electricity Electrochemical processes Fuel cells Hydrogen Hydrogen storage

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ieee SSCI 2014 - 2014 ieee symposium Series on Computational Intelligence - adprl 2014: 2014 ieee symposium on Adaptive dynamic programming and reinforcement learning, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 ieee symposium on Adaptive dynamic programming and reinforcement learning, adprl 2014

ISBN: (纸本)9781479945535

The proceedings contain 42 papers. The topics discussed include: approximate real-time optimal control based on sparse Gaussian process models;subspace identification for predictive state representation by nuclear norm minimization;active learning for classification: an optimistic approach;convergent reinforcement learning control with neural networks and continuous action search;theoretical analysis of a reinforcement learning based switching scheme;an analysis of optimistic, best-first search for minimax sequential decision making;information-theoretic stochastic optimal control via incremental sampling-based algorithms;policy gradient approaches for multi-objective sequential decision making: a comparison;and cognitive control in cognitive dynamic systems: a new way of thinking inspired by the brain.

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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： Boedecker, Joschka Springenberg, Jost Tobias Wuelfing, Jan Riedmiller, Martin Univ Freiburg Dept Comp Sci Machine Learning Lab D-79110 Freiburg Germany

ISBN: (纸本)9781479945528

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： Gaussian processes learning systems linear quadratic control manipulators nonlinear dynamical systems regression analysis GPR variance Gaussian process regression approximate real-time optimal control cart-pole system data-efficient learning algorithm iterative LQR formulation nonlinear systems receding horizon control sparse Gaussian process models system dynamics nonparametric model two-link manipulator Approximation algorithms Approximation methods Computational modeling Optimal control Optimization Predictive models Trajectory Gaussian processes Optimal control linear quadratic control Nonlinear systems learning systems Approximation method Nonlinear dynamical systems Approximation algorithms Manipulators Computational modeling Prediction models trajectory exploration strategy regression analysis Benchmark testing

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Policy Gradient Approaches for Multi-Objective Sequential Decision Making: A Comparison

Policy Gradient Approaches for Multi-Objective Sequential De...

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ieee symposium on Adaptive dynamic programming and reinforcement learning (adprl)

作者： Parisi, Simone Pirotta, Matteo Smacchia, Nicola Bascetta, Luca Restelli, Marcello Politecn Milan Dept Elect Informat & Bioengn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9781479945528

This paper investigates the use of policy gradient techniques to approximate the Pareto frontier in Multi-Objective Markov Decision Processes (MOMDPs). Despite the popularity of policy-gradient algorithms and the fact that gradient-ascent algorithms have been already proposed to numerically solve multi-objective optimization problems, especially in combination with multi-objective evolutionary algorithms, so far little attention has been paid to the use of gradient information to face multi-objective sequential decision problems. Three different Multi-Objective reinforcement-learning (MORL) approaches are here presented. The first two, called radial and Pareto following, start from an initial policy and perform gradient-based policy-search procedures aimed at finding a set of non-dominated policies. Differently, the third approach performs a single gradient-ascent run that, at each step, generates an improved continuous approximation of the Pareto frontier. The parameters of a function that defines a manifold in the policy parameter space are updated following the gradient of some performance criterion so that the sequence of candidate solutions gets as close as possible to the Pareto front. Besides reviewing the three different approaches and discussing their main properties, we empirically compare them with other MORL algorithms on two interesting MOMDPs.

关键词： Pareto optimisation approximation theory decision making evolutionary computation gradient methods learning (artificial intelligence) MOMDPs MORL approaches Pareto following Pareto frontier approximation gradient-ascent algorithms gradient-based policy-search procedures multiobjective Markov decision processes multiobjective evolutionary algorithms multiobjective optimization problems multiobjective reinforcement-learning approaches multiobjective sequential decision making nondominated policies performance criterion policy gradient approaches policy-gradient algorithms radial following Algorithm design and analysis Approximation algorithms Approximation methods Manifolds Measurement Optimization Water resources evolutionary algorithm Performance metrics Pareto optimisation Algorithm design and analysis Manifolds Approximation method gradient methods Approximation Theory Approximation algorithms Water Resources Policies decision making

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An adaptive dynamic programming algorithm to solve optimal control of uncertain nonlinear systems

An adaptive dynamic programming algorithm to solve optimal c...

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

作者： Xiaohong Cui Yanhong Luo Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (纸本)9781479945511

In this paper, an approximate optimal control method based on adaptive dynamic programming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neural network systems, where the criticaction networks approximate the optimal value function and optimal control and the other two neural networks approximates the unknown system. Furthermore the adaptive tuning laws are given based on Lyapunov approach, which ensures the uniform ultimate bounded stability of the closed-loop system. Finally, the effectiveness is demonstrated by a simulation example.

关键词： Optimal control Artificial neural networks Mathematical model Equations Heuristic algorithms Function approximation

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Near-optimality bounds for greedy periodic policies with application to grid-level storage

Near-optimality bounds for greedy periodic policies with app...

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

作者： Yuhai Hu Boris Defourny Department of Industrial & Systems Engineering Lehigh University USA

This paper is concerned with periodic Markov Decision Processes, as a simplified but already rich model for nonstationary infinite-horizon problems involving seasonal effects. Considering the class of policies greedy for periodic approximate value functions, we establish improved near-optimality bounds for such policies, and derive a corresponding value-iteration algorithm suitable for periodic problems. The effectiveness of a parallel implementation of the algorithm is demonstrated on a grid-level storage control problem that involves stochastic electricity prices following a daily cycle.

关键词： Silicon Markov processes Approximation algorithms Approximation methods Modeling Electricity dynamic programming

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