A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS). Actor-Critic learning is used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to improve the learning efficiency, a single RBF neural network is used to approximate the policy function of Actor and the value function of Critic simultaneously. The inputs of RBF network are the system error, as well as the first and the second-order differences of error. The Actor can realize the mapping from the system state to PID parameters, while the Critic evaluates the outputs of the Actor and produces TD error. Based on TD error performance index and gradient descent method, the updating rules of RBF kernel function and network weights were given. Simulation results show that the proposed controller is efficient for WECS and it is perfectly adaptable and strongly robust, which is better than that of a conventional PID controller.


    Access

    Download


    Export, share and cite



    Title :

    Adaptive PID Controller based on Reinforcement Learning for Wind Turbine Control


    Contributors:

    Publication date :

    2008-01-23


    Remarks:

    oai:zenodo.org:1057789



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629






    Self-adaptive Torque Vectoring Controller Using Reinforcement Learning

    Taherian, Shayan / Kuutti, Sampo / Visca, Marco et al. | IEEE | 2021



    ADAPTIVE LONGITUDINAL CONTROL USING REINFORCEMENT LEARNING

    PATHAK SHASHANK / NADKARNI VIJAY JAYANT / BAG SUVAM | European Patent Office | 2019

    Free access