7 papers
When Persistency is not Exciting in Data-Driven Predictive Control
Gianluca Giacomelli, Chuyu Lu, Siep Weiland +1
Understanding how to collect data that is meaningful for control purposes is of paramount importance in data-driven control. While existing approaches have primarily relied on the…
Unifying Controller Design for Stabilizing Nonlinear Systems with Norm-Bounded Control Inputs
Ming Li, Zhiyong Sun, Siep Weiland
This paper revisits a classical challenge in the design of stabilizing controllers for nonlinear systems with a norm-bounded input constraint. By extending Lin-Sontag's universal f…
Scalable Nonlinear DeePC: Bridging Direct and Indirect Methods and Basis Reduction
Thomas O. de Jong, Mircea Lazar, Siep Weiland +1
This paper studies regularized data-enabled predictive control (DeePC) within a nonlinear framework and its relationship to subspace predictive control (SPC). The -regularizati…
Constrained Performance Boosting Control for Nonlinear Systems
Gianluca Giacomelli, Danilo Saccani, Siep Weiland +2
We present the Alternating Direction Method of Multipliers (ADMM) for Performance Boosting (PB), an approach for designing neural controllers for stable nonlinear systems subject t…
A Tunable Universal Formula for Safety-Critical Control
Ming Li, Zhiyong Sun, Patrick J. W. Koelewijn +1
Sontag's universal formula is a widely used technique for stabilizing control through control Lyapunov functions. Recently, it has been extended to address safety-critical control…
A Comparative Study of Artificial Potential Fields and Reciprocal Control Barrier Function-based Safety Filters
Ming Li, Zhiyong Sun
In this paper, we demonstrate that controllers designed by artificial potential fields (APFs) can be derived from reciprocal control barrier function quadratic program (RCBF-QP) sa…