Robust Nonlinear Control Design State Space And Lyapunov Techniques Systems Control Foundations Applications New! Instant

Traditional control theory often relies on "linearization"—simplifying a system around a specific operating point. While this works for steady-state cruise control, it fails during aggressive maneuvers or when the system moves far from its equilibrium.

A promising frontier: combined with CLFs to simultaneously guarantee stability, robustness, and safety in a unified state-space framework. MPC solves an online optimization problem over a

MPC solves an online optimization problem over a finite horizon. However, without care, it can destabilize nonlinear systems. The robust solution: add a . At each step, enforce (V(\mathbfx_k+1) \leq V(\mathbfx_k) - \alpha V(\mathbfx_k)). This Lyapunov-based MPC (LMPC) guarantees closed-loop stability even with model mismatch, provided the terminal cost is a CLF. At each step, enforce (V(\mathbfx_k+1) \leq V(\mathbfx_k) -

) remains negative even when the system encounters its worst-case disturbances. Key Methodologies in Foundations & Applications Key Methodologies in Foundations & Applications