This article develops a new human-machine perception interface method to convert visual patterns to accurate eddy-current stimulation using an electromagnet (EM) array. The eddy-current stimulation is formulated as a feedforward controller design. In this paper, a state-space model for the eddy-current stimulation is derived for design and analysis of the controller. Unlike traditional methods where the distributed parameter systems are often modeled using partial differential equations and solved numerically using numerical methods such as finite element analysis, the model presented here offers closed-form solutions in state-space representation. The novel approach enables the applications of the well-established control theory for analyzing the system controllability. The feasibility and accuracy of the feedforward control method are numerically illustrated and validated by generating the stimulation with two types of patterns, which provides an essential base for future research of human-machine perception interface.
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Dynamics and Control of a Magnetic Transducer Array Using Multi-Physics Models and Artificial Neural Networks
A linear mechanical oscillator is non-linearly coupled with an electromagnet and its driving circuit through a magnetic field. The resulting non-linear dynamics are investigated using magnetic circuit approximations without major loss of accuracy and in the interest of brevity. Different computational approaches to simulate the setup in terms of dynamical system response and design parameters optimization are pursued. A current source operating in baseband without modulation directly feeds the electromagnet, which consists commonly of a solenoid and a horseshoe-shaped core. The electromagnet is then magnetically coupled to a mass made of soft magnetic material and attached to a spring with damping. The non-linear system is described by a linearized steady-space representation while is examined for controllability and observability. A controller using a pole placement approach is built to stabilize the element. Drawing upon the fact that coupling works both ways, enabling estimation of the mass position and velocity (state variables) by processing the induced voltage across the electromagnet, a state observer is constructed. Accurate and fast tracking of the state variables, along with the possibility of driving more than one module from the same source using modulation, proves the applicability of the electro-magneto-mechanical transducer for sensor applications. Next, a three-layer feed-forward artificial neural network (ANN) system equivalent was trained using the non-linear plant-linear controller-linear observer configuration. Simulations to investigate the robustness of the system with respect to different equilibrium points and input currents were carried out. The ANN proved robust with respect to position accuracy.
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- Award ID(s):
- 1809182
- PAR ID:
- 10344584
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 21
- Issue:
- 20
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 6788
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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