This article proposes an adaptive, optimal, data-driven control approach based on reinforcementlearning and adaptivedynamicprogramming to the three-phase grid-connected inverter employed in virtual synchronous gene...
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This article proposes an adaptive, optimal, data-driven control approach based on reinforcementlearning and adaptivedynamicprogramming to the three-phase grid-connected inverter employed in virtual synchronous generators (VSGs). This article takes into account unknown system dynamics and different grid conditions, including balanced/unbalanced grids, voltage drop/sag, and weak grids. The proposed method is based on value iteration, which does not rely on an initial admissible control policy for learning. Considering the premise that the VSG control should stabilize the closed-loop dynamics, the VSG outputs are optimally regulated through the adaptive, optimal control strategy proposed in this article. Comparative simulations and experimental results validate the proposed method's effectiveness and reveal its practicality and implementation.
This paper proposed a data-driven adaptive optimal control approach for CVCF (constant voltage, constant frequency) inverter based on reinforcementlearning and adaptivedynamicprogramming (ADP). Different from exist...
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This paper proposed a data-driven adaptive optimal control approach for CVCF (constant voltage, constant frequency) inverter based on reinforcementlearning and adaptivedynamicprogramming (ADP). Different from existing literature, the load is treated as a dynamic uncertainty and a robust optimal state-feedback controller is proposed. The stability of the inverter-load system has been strictly analyzed. In order to obtain accurate output current differential signal, this paper designs a tracking differentiator. It is ensured that the tracking error asymptotically converges to zero through the proposed output-feedback controllers. A standard proportional integral controller and linear active disturbance rejection control strategy are also designed for the purpose of comparison. The simulation results show that the proposed controller has inherent robustness and does not require retuning with different applications.
Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resourceconstrained environments, these IoT devices fac...
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The past decade has witnessed a surge in research activities related to adaptivedynamicprogramming (ADP) and reinforcementlearning (RL), particularly for control applications. Several books [item 1)–5) in the Appe...
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The past decade has witnessed a surge in research activities related to adaptivedynamicprogramming (ADP) and reinforcementlearning (RL), particularly for control applications. Several books [item 1)–5) in the Appendix] and survey papers [item 6)–10) in the Appendix] have been published on the subject. Both ADP and RL provide approximate solutions to dynamicprogramming problems. In a 1995 article by Barto et al. [item 11) in the Appendix], they introduced the so-called “adaptive real-time dynamicprogramming,” which was specifically to apply ADP for real-time control. Later, in 2002, Murray et al. [item 12) in the Appendix] developed an ADP algorithm for optimal control of continuous-time affine nonlinear systems. On the other hand, the most famous algorithms in RL are the temporal difference algorithm [item 13) in the Appendix] and the Q-learning algorithm [item 14) and 15) in the Appendix].
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 nor...
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 reinforcementlearning control with neural networks and continuous action search;theoretical analysis of a reinforcementlearning 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.
Deep reinforcementlearning is a focus research area in artificial intelligence. The principle of optimality in dynamicprogramming is a key to the success of reinforcementlearning methods. The principle of adaptive ...
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The proceedings contain 28 papers. The topics discussed include: local stability analysis of high-order recurrent neural networks with multi-step piecewise linear activation functions;finite-horizon optimal control de...
ISBN:
(纸本)9781467359252
The proceedings contain 28 papers. The topics discussed include: local stability analysis of high-order recurrent neural networks with multi-step piecewise linear activation functions;finite-horizon optimal control design for uncertain linear discrete-time systems;adaptive optimal control for nonlinear discrete-time systems;optimal control for a class of nonlinear system with controller constraints based on finite-approximation-errors ADP algorithm;finite horizon stochastic optimal control of uncertain linear networked control system;real-time tracking on adaptive critic design with uniformly ultimately bounded condition;a novel approach for constructing basis functions in approximate dynamicprogramming for feedback control;and a combined hierarchical reinforcementlearning based approach for multi-robot cooperative target searching in complex unknown environments.
In this paper, an approximate optimal control method based on adaptivedynamicprogramming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neu...
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ISBN:
(纸本)9781479945528
In this paper, an approximate optimal control method based on adaptivedynamicprogramming(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.
An online reinforcementlearning algorithm is proposed in this paper to directly utilizes online data efficiently for continuous deterministic systems without system parameters. The dependence on some specific approxi...
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ISBN:
(纸本)9781479945528
An online reinforcementlearning algorithm is proposed in this paper to directly utilizes online data efficiently for continuous deterministic systems without system parameters. The dependence on some specific approximation structures is crucial to limit the wide application of online reinforcementlearning algorithms. We utilize the online data directly with the kd-tree technique to remove this limitation. Moreover, we design the algorithm in the Probably Approximately Correct principle. Two examples are simulated to verify its good performance.
For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptivedynamicprogramming is proposed in this paper. By system transformation, the optimal tracking...
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ISBN:
(纸本)9781479945528
For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptivedynamicprogramming 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.
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