Michael G. Spencer
Papers
1
Total Citations
2
H-Index
1
About
Michael G. Spencer is a researcher whose work sits at the intersection of adaptive control systems, neural networks, and smart structures, with a particular focus on rotorcraft dynamics. His most-cited paper, "Adaptive nonlinear neural network controller for rotorcraft vibration" (1997), introduces a novel adaptive nonlinear neural network control algorithm designed to work in tandem with smart structure actuators and sensors to actively suppress vibrations in rotor blades. A key insight of this work is the formal analogy between the dynamic equations of motion for a rotor blade and those of a multilink robotic manipulator, allowing Spencer to apply established robot control strategies to the challenging problem of helicopter vibration. While his citation count is modest, this foundational contribution demonstrates an early and innovative application of neural network-based adaptive control to aerospace structures, bridging the gap between theoretical control methods and practical vibration suppression in complex, real-world systems.
Research Focus
Key Achievements
Top Papers
- 1