Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems

• URL: http://arxiv.org/abs/2604.24706v1
• Date: Mon, 27 Apr 2026 17:14:17 GMT
• Title: Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems
• Abstract summary: We propose a learning-based controller that exploits differential flatness, a property of many robotic systems.<n>Our approach uses a system extension and block-diagonal cost formulation to control general multi-input, nonlinear, affine systems.
• Score: 7.1560498064883875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based control techniques use data from past trajectories to control systems with uncertain dynamics. However, learning-based controllers are often computationally inefficient, limiting their practicality. To address this limitation, we propose a learning-based controller that exploits differential flatness, a property of many robotic systems. Recent research on using flatness for learning-based control either is limited in that it (i) ignores input constraints, (ii) applies only to single-input systems, or (iii) is tailored to specific platforms. In contrast, our approach uses a system extension and block-diagonal cost formulation to control general multi-input, nonlinear, affine systems. Furthermore, it satisfies input and half-space flat state constraints and guarantees probabilistic Lyapunov decrease using only two sequential convex optimizations. We show that our approach performs similarly to, but is multiple times more efficient than, a Gaussian process model predictive controller in simulation, and achieves competitive tracking in real hardware experiments.

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