Tetsu HORIUCHI
Papers
2
Total Citations
23
H-Index
2
About
Tetsu Horiuchi is a control theorist whose work bridges iterative learning, optimal control, and Hamiltonian systems. His research focuses on developing novel algorithms that exploit the intrinsic symmetric properties of Hamiltonian dynamics to solve constrained optimal control problems. Horiuchi’s most influential contribution, "Optimal control of Hamiltonian systems with input constraints via iterative learning" (2004, 19 citations), introduces an iterative learning algorithm that leverages the variational symmetry of Hamiltonian systems to efficiently handle input constraints—a persistent challenge in nonlinear control. He further refined this approach in "Iterative Learning Optimal Control of Hamiltonian Systems Based on Variational Symmetry" (2008, 4 citations), where he formalizes how the input-output mapping’s symmetry enables a systematic, numerically tractable method for solving optimal control problems without requiring explicit model inversion. Though his citation counts are modest, Horiuchi’s work is notable for its mathematical elegance and practical relevance to robotics, aerospace, and energy systems where Hamiltonian structures naturally arise. His contributions offer a principled framework for combining learning-based control with physical system invariants, making his research a valuable reference for students and engineers seeking to integrate iterative learning with optimal control theory.
Research Focus
Key Achievements
Top Papers
- 1
- 2