This article deals with the closed-loop optimal control of mechatronic devices based on dielectric elastomer membranes. The goal is to minimize the input electrical energy required to achieve a given position regulation task. The actuator is modeled based on a free-energy framework, which provides a thermodynamically consistent characterization of the losses that occur during actuation. Due to the strongly nonlinear behavior of both system model and dissipation function, traditional techniques based on the analytical solution of the Hamilton–Jacobi–Bellman (HJB) equation cannot be applied. Therefore, a reinforcement learning-based algorithm is here proposed as a tool to solve, offline, the HJB equation related to the energy minimization problem. After discussing the theory, experimental results are presented to validate the effectiveness of the proposed approach for different positioning tasks.

中文翻译：

---

基于强化学习的介电弹性体致动器最小能量位置控制

本文涉及基于介电弹性体膜的机电设备的闭环优化控制。目标是最小化实现给定位置调节任务所需的输入电能。致动器是基于自由能框架建模的，它提供了致动过程中发生的损失的热力学一致表征。由于系统模型和耗散函数的强非线性行为，无法应用基于 Hamilton-Jacobi-Bellman (HJB) 方程解析解的传统技术。因此，这里提出了一种基于强化学习的算法，作为离线求解与能量最小化问题相关的 HJB 方程的工具。讨论完理论后，