Robust model predictive control using reduced-order nonlinear models
Full-Order Safety for Nonlinear Reduced-Order MPC
Robotics
Summary
Controlling complex systems often requires simplified models to speed up calculations, but these simplifications can cause control errors that break safety rules. The authors introduce CoRE-MPC, a control method that uses a simpler model but still guarantees the full system stays safe and respects constraints. Their approach tracks and bounds prediction errors in a way that keeps the system running safely without needing to simulate the full complexity every time. They tested their method on various large systems, including a soft robot, showing it is much faster than existing robust methods while maintaining safety.
What this means in practice
- •For robot controllers: Maintain safety constraints during real-time control of complex soft robots using fast, lower-dimensional models with guaranteed full-system safety.
- •For industrial automation engineers: Implement efficient and reliable model predictive control on high-dimensional nonlinear processes with formal safety guarantees and low computational load.
Authors
Luis Pabon, Johannes Köhler, Revant Kasichainula, Marco Pavone
Abstract
Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can lead to constraint violations on the real system. We introduce CoRE-MPC, a robust MPC formulation that plans with a nonlinear ROM and guarantees recursive feasibility and constraint satisfaction for the full-order system. It can be applied with a broad class of nonlinear model reduction techniques and the resulting MPC does not require evaluating the full-order model online, thus ensuring computational efficiency. The key insight is in how the ROM affects the dynamics of the prediction error. We account for this with two error radii that are propagated alongside the ROM and provide a guaranteed bound on the prediction error. The approach is validated in simulation on an 80-state oscillator chain, a Burgers equation, and a 9,768-state finite-element soft robot, where a two-state ROM with data-driven bounds keeps all 1,628 nodes clear of a wall at a median solve time below 1 ms for a 20 ms control period. Across these examples, CoRE-MPC is 13 to 61 times faster than full-order robust MPC at only 27 to 35% higher closed-loop cost, whereas existing certified schemes are either infeasible or at least twice as conservative. Code: [released upon publication].