Safe Data-Driven Control and Dynamical Learning via Constrained Neural Architectures and Koopman Operators
For safety-critical control systems, this work provides a method to certify stability of learned models, addressing a key limitation of standard regression approaches.
This paper develops a framework combining Neural ODEs, geometric structures, and Koopman operator theory to produce data-driven control models with computable stability certificates. It achieves up to 61% reduction in tracking RMSE and 54% reduction in state estimation error on vehicle and oscillator benchmarks.
The deployment of learning-based models in safety-critical control systems demands mathematical guarantees that standard regression architectures cannot provide. This paper presents an integrated framework that bridges Neural Ordinary Differential Equations (Neural ODEs), measurement-induced geometric structures, and Koopman operator theory, with the explicit aim of producing data-driven models whose stability certificates are computable, not merely conjectured. Three complementary components are developed and analyzed. First, ControlSynth Neural ODEs enforce global convergence through tractable linear matrix inequalities (LMIs), enabling complex nonlinear dynamics to be captured without sacrificing boundedness guarantees. Second, the ICODE formulation incorporates extrinsic environmental inputs into the learned vector field, while measurement-induced bundle structures confine state trajectories to physically admissible manifolds. Third, a systematic ISS verification pipeline certifies the input-to-state stability of Koopman-identified models via a convex $L_2$-gain LMI, converting an otherwise intractable robustness question into a solvable semidefinite program. The certified model is embedded in an ICODE-MPPI controller, which uses continuous-time residual learning inside a stochastic sampling loop to deliver robust path tracking under parametric uncertainty and persistent disturbances. Numerical experiments on a vehicle path-tracking benchmark and a nonlinear mechanical oscillator demonstrate up to a 61\% reduction in tracking RMSE and a 54\% reduction in state estimation error relative to uncertified baselines, with near-zero LMI violation rates across all evaluated disturbance levels.