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Approximate dynamic programming for Optimal Stationary Control with Control-Dependent Noise

ieee TRANSACTIONS ON NEURAL NETWORKS 2011年第12期22卷 2392-2398页

作者： Jiang, Yu Jiang, Zhong-Ping NYU Polytech Inst Dept Elect & Comp Engn Brooklyn NY 11201 USA Beijing Univ Coll Engn Beijing 100214 Peoples R China

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Ito calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.

关键词： Approximate dynamic programming control-dependent noise optimal stationary control stochastic systems

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Intelligent Control of Grid-Connected Microgrids: An adaptive Critic-Based Approach

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ieee JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2015年第2期3卷 493-504页

作者： Seidi, Sima Bakhshai, Alireza Queens Univ Queens Ctr Energy & Power Elect Res Kingston ON K7L 3N6 Canada Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada

This paper presents an adaptive and intelligent power control approach for microgrid systems in the gridconnected operation mode. The proposed critic-based adaptive control system contains a neuro-fuzzy controller and a fuzzy critic agent. The fuzzy critic agent employs a reinforcement learning algorithm based on neuro-dynamic programming. The system feedback is made available to the critic agent's input as the controller's action in the previous state. The evaluation or reinforcement signal produced by the critic agent together with the back-propagation of error is then used for online tuning of the output layer weights of the neuro-fuzzy controller. The proposed controller shows superior results compared with the traditional PI control. The transient response time is significantly reduced, power oscillations are eliminated, and fast convergence is achieved. The simple design and improved dynamic behavior of the proposed controller make it a promising nominee for power control of microgrid systems.

关键词： Critic-based learning microgrids neuro-fuzzy control synchronous reference frame voltage sourced converters (VSCs)

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Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof

Discrete-time nonlinear HJB solution using approximate dynam...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Al-Tamimi, Asma Lewis, Frank Univ Texas Automat & Robot Res Inst Ft Worth TX 76118 USA Univ Texas Arlington Automat & Robot Res Inst Ft Worth TX 76118 USA

ISBN: (纸本)9781424407064

In this paper, a greedy iteration scheme based on approximate dynamic programming (ADP), namely Heuristic dynamic programming (HDP), is used to solve for the value function of the Hamilton Jacobi Bellman equation (HJB) that appears in discrete-time (DT) nonlinear optimal control. Two neural networks are used- one to approximate the value function and one to approximate the optimal control action. The importance of ADP is that it allows one to solve the HJB equation for general nonlinear discrete-time systems by using a neural network to approximate the value function. The importance of this paper is that the proof of convergence of the HDP iteration scheme is provided using rigorous methods for general discrete-time nonlinear systems with continuous state and action spaces. Two examples are provided in this paper. The first example is a linear system, where ADP is found to converge to the correct solution of the Algebraic Riccati equation (ARE). The second example considers a nonlinear control system.

关键词： adaptive critics approximate dynamic programming HJB policy iterations.

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Self-Triggered Approximate Optimal Neuro-Control for Nonlinear Systems Through adaptive dynamic programming

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第3期36卷 4713-4723页

作者： Zhao, Bo Zhang, Shunchao Liu, Derong Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou 510521 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this article, a novel self-triggered approximate optimal neuro-control scheme is presented for nonlinear systems by utilizing adaptive dynamic programming (ADP). According to the Bellman principle of optimality, the cost function of the general nonlinear system is approximated by building a critic neural network with a nested updating weight vector. Thus, the Hamilton-Jacobi-Bellman equation is solved to indirectly obtain the approximate optimal neuro-control input. In order to reduce the computation, the communication bandwidth, and the energy consumption, an appropriate self-triggering condition is designed as an alternative way to predict the updating time instants of the approximate optimal neuro-control policy. On the basis of Lyapunov's direct method, the stability of the closed-loop nonlinear system is analyzed and guaranteed to be uniformly ultimately bounded. Simulation results of two practical systems illustrate the present ADP-based self-triggered approximate optimal neuro-control scheme to be reasonable and effective.

关键词： adaptive dynamic programming (ADP) neural networks (NNs) optimal control reinforcement learning self-triggered control

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Online Optimization of Gear Shift and Velocity for Eco-Driving Using adaptive dynamic programming

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ieee TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第1期7卷 123-132页

作者： Li, Guoqiang Goerges, Daniel Wang, Meng Beijing Inst Technol Sch Mech Engn Beijing 10081 Peoples R China Univ Kaiserslautern Inst Electromobil Dept Elect & Comp Engn D-67663 Kaiserslautern Germany Delft Univ Technol Fac Civil Engn & Geosci Stevinweg 1 NL-2600 GA Delft Netherlands

In this paper a learning-based optimization method for online gear shift and velocity control is presented to reduce the fuel consumption and improve the driving comfort in a car-following process. The continuous traction force and the discrete gear shift are optimized jointly to improve both the powertrain operation and the longitudinal motion. The problem is formulated as a nonlinear mixed-integer optimization problem and solved based on adaptive dynamic programming. A major difference compared to existing approaches is that the developed control method is model-free, i.e. it does not rely on vehicle models. It can address system nonlinearities and adapt to changes in engine characteristics (e.g. consumption map) during vehicle driving. The computation is efficient and enables possible real-time implementation. The proposed control method is studied for an urban driving cycle to evaluate the control performance with respect to the fuel economy and the driving comfort. Simulations indicate that the host vehicle can reduce the fuel consumption by 5.03% and 1.12% for two consumption maps in comparison to the preceding while keeping a desired inter-vehicle distance. The results further show a decrease of 1.59% and 2.32% in fuel consumption compared to a linear quadratic controller with the same gear shift schedule.

关键词： Gears Engines Force Biological system modeling Optimization Fuel economy Vehicle dynamics Eco-driving gear shift schedule velocity optimization adaptive cruise control adaptive dynamic programming reinforcement learning

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adaptive Optimal Control via Q-learning for Ito Fuzzy Stochastic Nonlinear Continuous-Time Systems With Stackelberg Game

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ieee TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 2029-2038页

作者： Ming, Zhongyang Zhang, Huaguang Yan, Ying Yang, Liu Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

In order to solve the two-player Stackelberg game for the continuous-time nonlinear stochastic system, using the Takagi-Sugeno (T-S) fuzzy stochastic model, this paper defines the novel Q-functions and suggests an adaptive dynamic programming (ADP)-based approach that is completely model-free. First, based on the T-S fuzzy model, the overall fuzzy control policies with corresponding cost functions are designed where coupled penalty functions are considered. Subsequently, we create a novel two-level algorithm based on integral reinforcement learning and provide the proof of convergence to overcome challenge of computing the optimal cost functions analytically. On this basis, in order to achieve entirely model-free learning, which is the first attempt in solving fuzzy stochastic nonlinear continuous-time systems with the Stackelberg game problem, the innovative action-dependent Q-functions are developed. Fuzzy linearization technique and Q-learning algorithm are ingeniously combined in this article to solve their respective difficulties. In addition, the Lyapunov approach under the ADP-based control scheme ensures the stability of the closed-loop nonlinear stochastic system based on fuzzy approximation and is characterized by asymptotic stability. Finally, a numerical simulation is offered to show the efficacy of the existing ADP-based control technique.

关键词： Stochastic processes Games Q-learning Optimal control Stochastic systems Cost function Fuzzy systems adaptive dynamic programming (ADP) fuzzy stochastic nonlinear system Q-learning stackelberg game

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adaptive Interleaved reinforcement learning: Robust Stability of Affine Nonlinear Systems With Unknown Uncertainty

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第1期33卷 270-280页

作者： Li, Jinna Ding, Jinliang Chai, Tianyou Lewis, Frank L. Jagannathan, Sarangapani Liaoning Shihua Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76118 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

This article investigates adaptive robust controller design for discrete-time (DT) affine nonlinear systems using an adaptive dynamic programming. A novel adaptive interleaved reinforcement learning algorithm is developed for finding a robust controller of DT affine nonlinear systems subject to matched or unmatched uncertainties. To this end, the robust control problem is converted into the optimal control problem for nominal systems by selecting an appropriate utility function. The performance evaluation and control policy update combined with neural networks approximation are alternately implemented at each time step for solving a simplified Hamilton-Jacobi-Bellman (HJB) equation such that the uniformly ultimately bounded (UUB) stability of DT affine nonlinear systems can be guaranteed, allowing for all realization of unknown bounded uncertainties. The rigorously theoretical proofs of convergence of the proposed interleaved RL algorithm and UUB stability of uncertain systems are provided. Simulation results are given to verify the effectiveness of the proposed method.

关键词： Uncertainty Nonlinear systems Robust control Optimal control adaptive systems Robust stability Linear systems Interleaved reinforcement learning neural networks (NNs) robust control uncertain systems

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reinforcement learning-Based Cooperative Optimal Output Regulation via Distributed adaptive Internal Model

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第10期33卷 5229-5240页

作者： Gao, Weinan Mynuddin, Mohammed Wunsch, Donald C. Jiang, Zhong-Ping Florida Inst Technol Coll Engn & Sci Dept Mech & Civil Engn Melbourne FL 32901 USA Univ Cent Florida Dept Civil Environm & Construct Engn Major Transp Orlando FL 32816 USA Missouri Univ Sci & Technol Missouri S&T Dept Comp Engn Rolla MO 65409 USA NYU Tandon Sch Engn Dept Elect & Comp Engn Brooklyn NY 11201 USA

In this article, a data-driven distributed control method is proposed to solve the cooperative optimal output regulation problem of leader-follower multiagent systems. Different from traditional studies on cooperative output regulation, a distributed adaptive internal model is originally developed, which includes a distributed internal model and a distributed observer to estimate the leader's dynamics. Without relying on the dynamics of multiagent systems, we have proposed two reinforcement learning algorithms, policy iteration and value iteration, to learn the optimal controller through online input and state data, and estimated values of the leader's state. By combining these methods, we have established a basis for connecting data-distributed control methods with adaptive dynamic programming approaches in general since these are the theoretical foundation from which they are built.

关键词： Regulation Adaptation models Power system dynamics Multi-agent systems Vehicle dynamics Symmetric matrices Optimal control adaptive optimal control cooperative output regulation distributed adaptive internal model reinforcement learning

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An adaptive-learning framework for semi-cooperative multi-agent coordination

An adaptive-learning framework for semi-cooperative multi-ag...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Boukhtouta, Abdeslem Berger, Jean Powell, Warren B. George, Abraham Defence Research and Development Canada-Valcartier Quebec QC G3J 1X5 Canada Department of Operations Research and Financial Engineering Princeton University Princeton NJ 08544 United States

ISBN: (纸本)9781424498888

Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop a general mathematical model for distributed, semi-cooperative planning and suggest a solution strategy which involves decomposing the system into subproblems, each of which is specified at a certain period in time and controlled by an agent. The agents communicate marginal values of resources to each other, possibly with distortion. We design experiments to demonstrate the benefits of communication between the agents and show that, with communication, the solution quality approaches that of the ideal situation where the entire problem is controlled by a single agent. © 2011 ieee.

关键词： Multi agent systems

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An Intelligent and Secure Control Approach for Nonlinear Systems under Attacks

An Intelligent and Secure Control Approach for Nonlinear Sys...

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ieee symposium Series on Computational Intelligence (ieee SSCI)

作者： Zhong, Xiangnan Ni, Zhen Florida Atlantic Univ Dept Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9781728190488

This paper designs an intelligent and secure control approach based on adaptive dynamic programming for a class of nonlinear systems under the actuator attacks. The designed method can monitor and identify the attacks by an established state estimator based detector. When an attack is triggered, the control process automatically switches to a game-theoretical architecture for attack mitigation. The intelligent learning process is developed for both nominal and attack conditions without the requirement of system dynamics and attack information. Neural network techniques are applied to implement the proposed method with two critic networks and the control signals are calculated accordingly. Therefore, the designed intelligent control method can reduce the computation complexity. Simulation studies and results demonstrate the necessary of attack detection and mitigation during the learning process, and also verify the effectiveness of the developed method.

关键词： adaptive dynamic programming reinforcement learning attack mitigation intelligent control attack detection and identification

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