arXiv subjects

E. Lefeber

Publications and source records attributed to E. Lefeber.

3 recordsLinked to original sources

arXiv2025

High-Performance Trajectory Tracking MPC for Quadcopters with Coupled Time-Varying Constraints and Stability Proofs

In this paper, we present a cascade control structure to address the trajectory tracking problem for quadcopters, ensuring uniform global asymptotic stability of the state tracking error dynamics. An MPC strategy based on a 12-dimensional prediction model is proposed for the outer loop, explicitly accounting for time-varying coupled constraints, where multiple variables are interdependent and need to be handled together. The outer-loop controller generates an acceleration reference, which is then converted into attitude and angular velocity references, later tracked by a nonlinear inner-loop controller. Numerical simulations validate the approach, demonstrating enhanced performance in precise and fast tracking by imposing less conservative constraints than existing approaches, while still guaranteeing stability.

eess.SY↗

arXiv2024

Online Learning of Interaction Dynamics with Dual Model Predictive Control for Multi-Agent Systems Using Gaussian Processes

The control of a single agent in complex and uncertain multi-agent environments requires careful consideration of the interactions between the agents. In this context, this paper proposes a dual model predictive control (MPC) method using Gaussian process (GP) models for multi-agent systems. While Gaussian process MPC (GP-MPC) has been shown to be effective in predicting the dynamics of other agents, current methods do not consider the influence of the control input on the covariance of the predictions, and hence lack the dual control effect. Therefore, we propose a dual MPC that directly optimizes the actions of the ego agent, and the belief of the other agents by jointly optimizing their state trajectories as well as the associated covariance while considering their interactions through a GP. We demonstrate our GP-MPC method in a simulation study on autonomous driving, showing improved prediction quality compared to a baseline stochastic MPC. The results show that GP-MPC can learn the interactions between the agents online, demonstrating the potential of GPs for dual MPC in uncertain and unseen scenarios.

math.OC↗

arXiv2020

Traffic Equations for Fluid Networks with Overflows

Many real life queueing networks have finite buffers with overflows. To understand the behavior of such networks, we consider traffic equations that generalize the traffic equations of classic open queueing networks where some nodes are potentially overloaded. We present a novel, efficient algorithm for solving the equations for overflow networks together with a sufficient condition for existence and uniqueness of solutions. Our analysis also sharpens results of traffic equations for classic open queueing networks.

math.OC↗