Mirhan Urkmez

Mirhan Urkmez, Maryam Sharifi, Shahab Heshmati-Alamdari

This paper addresses connectivity preservation in leader-follower multi-agent systems with unknown control-affine dynamics and local state information. We introduce the distributed data-driven zeroing control barrier function (3D-ZCBF) framework, which ensures the controlled invariance of safety sets by identifying derivative bounds from input-state data without requiring explicit models of high-dimensional agent dynamics. In this work, we derive the explicit, decoupled safety conditions necessary to maintain connectivity for leader-leader, and follower-follower pairings. These individual constraints, along with the leader-follower conditions, are aggregated into explicit system-wide conditions that formally guarantee the preservation of the entire communication network. Furthermore, we provide a quantitative analysis demonstrating how the size of the collected data set and the accuracy of the learned Jacobian bounds impact the feasibility of the safety certificates. The proposed conditions are implemented via a projection-based controller, and simulations confirm that these explicit 3D-ZCBF requirements effectively maintain system-level connectivity using only local, two-hop information.

Mirhan Urkmez, Shahab Heshmati-Alamdari

We propose a distributionally robust data-driven predictive control framework for stochastic linear time-invariant systems with unknown dynamics and disturbance distributions. We use an offline trajectory to fit the subspace predictive control (SPC) predictor via least squares and construct an empirical distribution of the prediction residuals as a proxy for the unknown disturbance distribution. We then center a Wasserstein ambiguity set around this estimate and minimize the worst-case expected cost while enforcing probabilistic output constraint satisfaction over all distributions in the set. The resulting problem admits a tractable reformulation with an equivalent direct data-driven form, eliminating the need for explicit predictor identification. Using finite-sample concentration results, we provide a data-driven Wasserstein radius such that, with high probability, the true expected cost is bounded above by the tractable objective and output constraints are satisfied with respect to the true disturbance distribution. Numerical simulations validate the framework against existing methods under various disturbance conditions and cost functions.