TD3法によるスライディングモード制御のパラメータ決定

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  • Determination of Parameters for Sliding Mode Control using TD3

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<p>Reinforcement learning (RL) has been studied as an effective method for tasks without explicit training data. In recent years, it has been shown that deep RL (DRL), which introduces deep learning algorithms into RL, can be applied to more complex tasks. Recent studies have introduced DRL into sliding mode control (SMC), yielding important research results. However, the black box in the computational process of a neural network (NN) is a troublesome problem, especially when NNs treat control engineering. In this paper, we propose a novel approach to determine parameters for SMC using DRL. In this method, a policy model constructed to be equivalent to the equation of the SMC input is trained by a DRL method that can treat continuous action spaces. The results show that the proposed method can determine the SMC parameters that successfully control the ball beam system through numerical experiments.</p>

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