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Supervised learning for robust predictive control: Safe and tunable approach
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DOI：10.1016/j.neucom.2026.132637.png)
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Abstract
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• Paper presents a real-time tunable neural network-based approximate controller. • Approximate controller ensures constraints, recursive feasibility, and stability. • Lightweight, library-free controller design suitable for embedded hardware use. • Case study 1: Double integrator control, 94% less memory than tube MPC. • Case study 2: Quadrotor control, 99.5% less memory than explicit MPC.
Keywords:
Supervised learning
Model predictive control
Neural networks
Robust model predictive control
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