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

Control systems with uncertain dynamics can be managed using learning-based control techniques, which leverage data from past trajectories. However, these controllers are often hindered by computational inefficiency, limiting their real-world applicability. A new approach proposes exploiting differential flatness, a characteristic inherent to many robotic systems, to enhance the efficiency of learning-based model predictive control¹. By harnessing this property, the controller can better navigate constrained multi-input control affine systems. This development has significant implications for the control of complex systems, potentially leading to more efficient and adaptive control mechanisms. The application of differential flatness in learning-based control can mitigate the computational burdens associated with traditional methods, thereby increasing the practicality of these controllers in various domains. This matters to practitioners because more efficient learning-based control systems can lead to improved performance and reliability in robotic and autonomous systems, ultimately enhancing overall system security and resilience.