Moezi Seyed Alireza, Sedaghati Ramin, Rakheja Subhash
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC H3G 1M8, Canada.
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC H3G 1M8, Canada.
ISA Trans. 2024 Jan;144:245-259. doi: 10.1016/j.isatra.2023.10.030. Epub 2023 Oct 31.
In recent years, magnetoactive soft continuum robots (MSCRs) with multimodal locomotion capabilities have emerged for various biomedical applications. Developments in nonlinear dynamic models and effective control methods for MSCRs are deemed vital not only to gain a better understanding of their coupled magneto-mechanical behavior but also to accurately steer the MSCRs inside the human body. This study presents a novel dynamic model and model-based AI-driven control method to guide an MSCR in a fluidic environment. The MSCR is fully exposed to fluid flows at different rates to simulate the biofluidic environment within the body. A novel nonlinear dynamic model considering the effect of damping and drag force attributed to fluidic flows is first developed to accurately and efficiently predict the response of the MSCR under varying magnetic and mechanical loading. Fairly accurate correlations were observed between the theoretical responses based on the developed magneto-viscoelastic model and the experimental data for various scenarios. A novel model-based control algorithm based on a fractional-order sliding surface and deep reinforcement learning algorithm (DRL-FOSMC) is subsequently developed to accurately steer the magnetoactive soft robot on predefined trajectories considering varying fluid flow rates. A fractional-order sliding surface and a compensator, trained using the deep deterministic policy gradient algorithm, are designed to mitigate the amount of chattering and enhance the tracking performance of the closed-loop system. The stability proof of the developed control algorithm is also presented. A hardware-in-the-loop experimental framework has been designed to assess the effectiveness of the proposed control algorithm through various case studies. The performance of the proposed DRL-FOSMC algorithm is rigorously assessed and found to be superior when compared with other control methods.
近年来，具有多模态运动能力的磁活性软连续体机器人（MSCRs）已出现并应用于各种生物医学领域。MSCRs的非线性动力学模型和有效控制方法的发展不仅对于更好地理解其磁-机械耦合行为至关重要，而且对于在人体内部精确操控MSCRs也至关重要。本研究提出了一种新颖的动力学模型和基于模型的人工智能驱动控制方法，以在流体环境中引导MSCR。MSCR完全暴露于不同流速的流体流中，以模拟体内的生物流体环境。首先开发了一种考虑流体流动引起的阻尼和阻力影响的新型非线性动力学模型，以准确有效地预测MSCR在变化的磁和机械载荷下的响应。基于所开发的磁粘弹性模型的理论响应与各种情况下的实验数据之间观察到相当准确的相关性。随后，开发了一种基于分数阶滑模面和深度强化学习算法的新型基于模型的控制算法（DRL-FOSMC），以在考虑不同流体流速的情况下，将磁活性软机器人精确地引导到预定轨迹上。使用深度确定性策略梯度算法训练的分数阶滑模面和补偿器旨在减少抖振量并提高闭环系统的跟踪性能。还给出了所开发控制算法的稳定性证明。设计了一种硬件在环实验框架，通过各种案例研究来评估所提出控制算法的有效性。对所提出的DRL-FOSMC算法的性能进行了严格评估，发现与其他控制方法相比具有优越性。
