手法を比較

	バックステッピング制御	H-infinity制御 (H∞制御)	スライディングモード制御
分野	制御理論	制御理論	制御理論
系統	Machine learning	Machine learning	Machine learning
提唱年	1995	1981	1977
提唱者	Miroslav Krstic	George Zames	Vadim Utkin
種類	algorithm	algorithm	algorithm
原典	Krstic, M., Kanellakopoulos, I., & Kokotovic, P. (1995). Nonlinear and Adaptive Control Design. John Wiley & Sons.	Zames, G. (1981). Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverses. IEEE Transactions on Automatic Control, 26(2), 301-320.	Utkin, V. I. (1977). Variable structure systems with sliding modes. IEEE Transactions on Automatic Control, 22(2), 212-222.
別名	Integrator Backstepping, Recursive Lyapunov Design	H∞ Control, Robust Control, Minimax Control	SMC, Variable Structure Control, Robust Control with Discontinuities
関連	3	4	4
概要	Backstepping is a systematic nonlinear control design method that decomposes a complex nonlinear system into simpler subsystems and designs a controller recursively, layer by layer, ensuring stability at each step. Developed by Krstic, Kanellakopoulos, and Kokotovic, backstepping enables control of nonlinear systems without requiring exact model knowledge or full state linearization, combining flexibility with guaranteed stability.	H-infinity (H∞) control is a robust control method that minimizes the worst-case gain from disturbances to controlled outputs, formulated as a minimax optimization problem. Pioneered by Zames in the early 1980s, H∞ control provides a principled way to design feedback controllers that tolerate model uncertainty, unmodeled dynamics, and disturbances while maintaining stability and performance, making it essential for applications requiring guaranteed robustness.	Sliding Mode Control (SMC) is a robust nonlinear control technique that forces a system to follow a predetermined surface (the sliding surface) in state space by using discontinuous (bang-bang or high-frequency switching) control inputs. Developed by Utkin and further advanced by Slotine, SMC is remarkably insensitive to parameter variations and disturbances—once the system reaches the sliding surface, its behavior is determined solely by the surface geometry, not by uncertainty. This makes SMC powerful for nonlinear systems, manipulators, and uncertain systems where robustness is paramount.