Actuator Selection and Control of an Array of Electromagnetic Soft Actuators

Electromagneticsoft actuator arrays (ESAAs) combine compliance with fast, controllable actuation and scalability, providing a promising foundation for the development of interconnected soft actuator arrays inspired by the structure and function of biological muscles. In this work, we present a control framework and an actuator selection strategy for an artificial soft muscle composed of ESAAs to enable accurate reference tracking. Since directly measuring the states of each ESA is often impractical in real-world applications, we first design a Kalman filter-based observer to estimate all system states from available observations. Using these estimates, we develop a Linear Quadratic Gaussian (LQG) controller to achieve reference tracking. Since thermal buildup from constant use can damage the actuators, we consider whether switching between different subsets of active actuators could offer thermal relief. While actuator switching intuitively suggests reduced heating by providing resting periods, our investigation reveals that this strategy can lead to higher thermal accumulation compared to the continuous mode. This is because we need substantially larger control effort when we have fewer active actuators in the switching mode, which, in the absence of effective active cooling, fail to provide sufficient heat dissipation during operation. Simulation results are presented to demonstrate the effectiveness of the proposed method in achieving the trajectory objective and to explore how switching affects the system’s thermal profile, revealing a trade-off between tracking performance and heat generation.