Search papers, labs, and topics across Lattice.
This paper introduces a learning-based model predictive control (MPC) approach for multi-input control-affine systems that leverages differential flatness to improve computational efficiency. They formulate a system extension and block-diagonal cost structure to handle input and half-space flat state constraints while ensuring probabilistic Lyapunov decrease with only two sequential convex optimizations. The method demonstrates comparable performance to a Gaussian process MPC but with significantly improved computational speed, validated through simulations and real-world hardware experiments.
Achieve real-time learning-based control of complex robotic systems by exploiting differential flatness for dramatic speedups in MPC computation.
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.
