Full-Order Safety for Nonlinear Reduced-Order MPC
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Full-Order Safety for Nonlinear Reduced-Order MPC. The paper presents CoRE-MPC, a robust model predictive control approach that uses reduced-order models to ensure safety and constraint satisfaction for high-dimensional nonlinear systems. By incorporating data-driven err…
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · state_count: 80 other · Basis not reported · Differing source assertions
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · state_count: 9768 other · Basis not reported · Differing source assertions
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · node_count: 1628 other · Basis not reported
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · solve_time: 1 other · Basis not reported
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · control_period: 20 other · Basis not reported
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · speed_up_factor: 13 other · Basis not reported
“ach 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 c”
Exact source · revision 1Source owner not reported
Reported quantity · closed_loop_cost_increase: 27 other · Basis not reported
“ach 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 c”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“ertified schemes are either infeasible or at least twice as conservative. Code: [released upon publication].”
Exact source · revision 1Source owner not reported
Report timeline
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- arXiv Robotics — research abstractsFull-Order Safety for Nonlinear Reduced-Order MPC
The paper presents CoRE-MPC, a robust model predictive control approach that uses reduced-order models to ensure safety and constraint satisfaction for high-dimensional nonlinear systems. By incorporating data-driven error radii, the method guarantees recursive feasibility and provides error bounds. Validated on complex simulations, including a 9,768-state soft robot, CoRE-MPC achieves 13 to 61 times faster performance than full-order robust MPC with only a 27 to 35% increase in closed-loop cost.
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