Mathias Hudoba de Badyn
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View article: Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach
Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach Open
Data-enabled predictive control (DeePC) has emerged as a powerful technique to control complex systems without the need for extensive modeling efforts. However, relying solely on offline collected data trajectories to represent the system …
View article: Distributed Dual Quaternion Extended Kalman Filtering for Spacecraft Pose Estimation
Distributed Dual Quaternion Extended Kalman Filtering for Spacecraft Pose Estimation Open
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and rela…
View article: Online Feedback Equilibrium Seeking
Online Feedback Equilibrium Seeking Open
This paper proposes a unifying design framework for dynamic feedback controllers that track solution trajectories of time-varying generalized equations, such as local minimizers of nonlinear programs or competitive equilibria (e.g., Nash) …
View article: Self-Tuning Network Control Architectures with Joint Sensor and Actuator Selection
Self-Tuning Network Control Architectures with Joint Sensor and Actuator Selection Open
We formulate a mathematical framework for designing a self-tuning network control architecture, and propose a computationally-feasible greedy algorithm for online architecture optimization. In this setting, the locations of active sensors …
View article: Degradation-aware data-enabled predictive control of energy hubs
Degradation-aware data-enabled predictive control of energy hubs Open
Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy systems with batteries can improve the energy use of a bui…
View article: Degradation-aware data-enabled predictive control of energy hubs
Degradation-aware data-enabled predictive control of energy hubs Open
Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy systems with batteries can improve the energy use of a bui…
View article: Stability and Robustness of Distributed Suboptimal Model Predictive Control
Stability and Robustness of Distributed Suboptimal Model Predictive Control Open
In distributed model predictive control (MPC), the control input at each sampling time is computed by solving a large-scale optimal control problem (OCP) over a finite horizon using distributed algorithms. Typically, such algorithms requir…
View article: Stability and Robustness of Distributed Suboptimal Model Predictive Control
Stability and Robustness of Distributed Suboptimal Model Predictive Control Open
In distributed model predictive control (MPC), the control input at each sampling time is computed by solving a large-scale optimal control problem (OCP) over a finite horizon using distributed algorithms. Typically, such algorithms requir…
View article: Self-Tuning Network Control Architectures
Self-Tuning Network Control Architectures Open
We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all a…
View article: Online Feedback Equilibrium Seeking
Online Feedback Equilibrium Seeking Open
This paper proposes a unifying design framework for dynamic feedback controllers that track solution trajectories of time-varying generalized equations, such as local minimizers of nonlinear programs or competitive equilibria (e.g., Nash) …
View article: Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC Open
Because physics-based building models are difficult to obtain as each\nbuilding is individual, there is an increasing interest in generating models\nsuitable for building MPC directly from measurement data. Machine learning\nmethods have b…
View article: Distributed model predictive control of buildings and energy hubs
Distributed model predictive control of buildings and energy hubs Open
Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized…
View article: Discrete-Time Linear-Quadratic Regulation via Optimal Transport
Discrete-Time Linear-Quadratic Regulation via Optimal Transport Open
In this paper, we consider a discrete-time stochastic control problem with uncertain initial and target states. We first discuss the connection between optimal transport and stochastic control problems of this form. Next, we formulate a li…
View article: Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking Open
This paper proposes a general framework for constructing feedback controllers that drive complex dynamical systems to "efficient" steady-state (or slowly varying) operating points. Efficiency is encoded using generalized equations which ca…
View article: Physics-informed linear regression is a competitive approach compared to Machine Learning methods in building MPC
Physics-informed linear regression is a competitive approach compared to Machine Learning methods in building MPC Open
Because physics-based building models are difficult to obtain as each building is individual, there is an increasing interest in generating models suitable for building MPC directly from measurement data. Machine learning methods have been…
View article: Distributed Model Predictive Control of Buildings and Energy Hubs
Distributed Model Predictive Control of Buildings and Energy Hubs Open
Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized…
View article: Distributed Feedback Optimisation for Robotic Coordination
Distributed Feedback Optimisation for Robotic Coordination Open
Feedback optimisation is an emerging technique aiming at steering a system to an optimal steady state for a given objective function. We show that it is possible to employ this control strategy in a distributed manner. Moreover, we prove a…
View article: Discrete-Time Linear-Quadratic Regulation via Optimal Transport
Discrete-Time Linear-Quadratic Regulation via Optimal Transport Open
In this paper, we consider a discrete-time stochastic control problem with uncertain initial and target states. We first discuss the connection between optimal transport and stochastic control problems of this form. Next, we formulate a li…
View article: Decentralized trajectory optimization for multi-agent exploration
Decentralized trajectory optimization for multi-agent exploration Open
Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information d…
View article: Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration
Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration Open
ISSN:2377-3766
View article: Graph-theoretic optimization for edge consensus
Graph-theoretic optimization for edge consensus Open
We consider network structures that optimize the H2 norm of weighted, time scaled consensus networks, under a minimal representation of such consensus networks described by the edge Laplacian. We show that a greedy algorithm can be used to…
View article: Input Convex Neural Networks for Building MPC
Input Convex Neural Networks for Building MPC Open
Model Predictive Control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for buildings make data- driven modelling an attractive alternative …
View article: H2 Performance of Series-Parallel Networks: A Compositional Perspective
H2 Performance of Series-Parallel Networks: A Compositional Perspective Open
ISSN:0018-9286
View article: Graph-Theoretic Optimization for Edge Consensus
Graph-Theoretic Optimization for Edge Consensus Open
We consider network structures that optimize the $\mathcal{H}_2$ norm of weighted, time scaled consensus networks, under a minimal representation of such consensus networks described by the edge Laplacian. We show that a greedy algorithm c…
View article: Performance and Design of Consensus on Matrix-Weighted and Time-Scaled Graphs
Performance and Design of Consensus on Matrix-Weighted and Time-Scaled Graphs Open
ISSN:2325-5870
View article: Input Convex Neural Networks for Building MPC
Input Convex Neural Networks for Building MPC Open
Model Predictive Control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for buildings make data-driven modelling an attractive alternative i…
View article: Time Scale Design for Network Resilience
Time Scale Design for Network Resilience Open
In this paper we consider the $\mathcal{H}_2$-norm of networked systems with multi-time scale consensus dynamics. We develop a general framework for such systems that allows for edge weighting, independent agent-based time scales, as well …
View article: Efficient Computation of H<sub>2</sub> Performance on Series-Parallel Networks
Efficient Computation of H<sub>2</sub> Performance on Series-Parallel Networks Open
Series-parallel networks are a class of graphs on which many NP-hard problems have tractable solutions. In this paper, we examine performance measures on leader-follower consensus on series-parallel networks. We show that a distributed com…
View article: Efficient Computation of $\mathcal{H}_2$ Performance on Series-Parallel Networks
Efficient Computation of $\mathcal{H}_2$ Performance on Series-Parallel Networks Open
Series-parallel networks are a class of graphs on which many NP-hard problems have tractable solutions. In this paper, we examine performance measures on leader-follower consensus on series-parallel networks. We show that a distributed com…