Lukas Hewing
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View article: Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods
Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods Open
Artificial Intelligence techniques have developed into a transformative force across many industries. Their industrial adaption in aerospace Guidance, Navigation and Control (GNC) systems, however, has been rather limited to date. The “Art…
View article: Sequential Convex Programming for Optimal Line of Sight Steering in Agile Missions
Sequential Convex Programming for Optimal Line of Sight Steering in Agile Missions Open
The trend toward onboard autonomy and spacecraft minimization present significant potential for advances in efficient Line of Sight management by making optimal use of the limited torque resources available. At SENER Aeroespacial, we are i…
View article: Contextual Tuning of Model Predictive Control for Autonomous Racing
Contextual Tuning of Model Predictive Control for Autonomous Racing Open
Learning-based model predictive control has been widely applied in autonomous racing to improve the closed-loop behaviour of vehicles in a data-driven manner. When environmental conditions change, e.g., due to rain, often only the predicti…
View article: Volume Control of Low-Cost Ventilator with Automatic Set-Point Adaptation
Volume Control of Low-Cost Ventilator with Automatic Set-Point Adaptation Open
This paper considers the control design for a low-cost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is physically unable to breathe. First, it experimentally show…
View article: Systematic Design, Control, and Parametric Testing of an Automated Resuscitator Bag Mechanical Ventilator
Systematic Design, Control, and Parametric Testing of an Automated Resuscitator Bag Mechanical Ventilator Open
The COVID-19 crisis has revealed and exacerbated a shortage of mechanical ventilators in hospitals around the world, regardless of their government’s resources. Where some countries can respond to the situation by ordering more high-end ve…
View article: Cautious Model Predictive Control Using Gaussian Process Regression
Cautious Model Predictive Control Using Gaussian Process Regression Open
ISSN:1063-6536
View article: Volume Control of Low-Cost Ventilator with Automatic Set-Point\n Adaptation
Volume Control of Low-Cost Ventilator with Automatic Set-Point\n Adaptation Open
This paper considers the control design for a low-cost ventilator that is\nbased on a manual resuscitator bag (also known as AmbuBag) to pump air into the\nlungs of a patient who is physically unable to breathe. First, it\nexperimentally s…
View article: Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction Open
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and opti…
View article: Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty Open
ISSN:2640-3498
View article: On Simulation and Trajectory Prediction with Gaussian Process Dynamics
On Simulation and Trajectory Prediction with Gaussian Process Dynamics Open
Established techniques for simulation and prediction with Gaussian process (GP) dynamics often implicitly make use of an independence assumption on successive function evaluations of the dynamics model. This can result in significant error…
View article: Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty Open
Designing controllers for systems affected by model uncertainty can prove to be a challenge, especially when seeking the optimal compromise between the conflicting goals of identification and control. This trade-off is explicitly taken int…
View article: Learning-Based Model Predictive Control: Toward Safe Learning in Control
Learning-Based Model Predictive Control: Toward Safe Learning in Control Open
Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Mo…
View article: Scenario-Based Probabilistic Reachable Sets for Recursively Feasible Stochastic Model Predictive Control
Scenario-Based Probabilistic Reachable Sets for Recursively Feasible Stochastic Model Predictive Control Open
ISSN:2475-1456
View article: Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm
Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm Open
ISSN:2377-3766
View article: Learning-Based Model Predictive Control for Autonomous Racing
Learning-Based Model Predictive Control for Autonomous Racing Open
ISSN:2377-3766
View article: Probabilistic model predictive safety certification for learning-based\n control
Probabilistic model predictive safety certification for learning-based\n control Open
Reinforcement learning (RL) methods have demonstrated their efficiency in\nsimulation environments. However, many applications for which RL offers great\npotential, such as autonomous driving, are also safety critical and require a\ncertif…
View article: Probabilistic model predictive safety certification for learning-based control
Probabilistic model predictive safety certification for learning-based control Open
Reinforcement learning (RL) methods have demonstrated their efficiency in simulation environments. However, many applications for which RL offers great potential, such as autonomous driving, are also safety critical and require a certified…
View article: Recursively Feasible Stochastic Model Predictive Control using Indirect\n Feedback
Recursively Feasible Stochastic Model Predictive Control using Indirect\n Feedback Open
We present a stochastic model predictive control (MPC) method for linear\ndiscrete-time systems subject to possibly unbounded and correlated additive\nstochastic disturbance sequences. Chance constraints are treated in analogy to\nrobust M…
View article: Stochastic Model Predictive Control for Linear Systems Using Probabilistic Reachable Sets
Stochastic Model Predictive Control for Linear Systems Using Probabilistic Reachable Sets Open
In this paper we propose a stochastic model predictive control (MPC)\nalgorithm for linear discrete-time systems affected by possibly unbounded\nadditive disturbances and subject to probabilistic constraints. Constraints are\ntreated in an…
View article: Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars
Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars Open
This paper presents an adaptive high performance control method for\nautonomous miniature race cars. Racing dynamics are notoriously hard to model\nfrom first principles, which is addressed by means of a cautious nonlinear\nmodel predictiv…
View article: Stochastic Model Predictive Control for Linear Systems using Probabilistic Reachable Sets
Stochastic Model Predictive Control for Linear Systems using Probabilistic Reachable Sets Open
In this paper, we propose a stochastic model predictive control (MPC) algorithm for linear discrete-time systems affected by possibly unbounded additive disturbances and subject to probabilistic constraints. Constraints are treated in anal…
View article: Cautious NMPC with Gaussian Process Dynamics for Miniature Race Cars.
Cautious NMPC with Gaussian Process Dynamics for Miniature Race Cars. Open
This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive c…
View article: Robust gain-scheduled control of variable stiffness actuators
Robust gain-scheduled control of variable stiffness actuators Open
Variable stiffness actuators were introduced to decouple an otherwise stiff actuator from the load by an adjustable elasticity. This variable elastic element can be used as torque sensor, acts as an energy storage, decouples the actuator f…