Lukas Hewing

Last Name

Hewing

First Name

Lukas

ORCID

0000-0002-2770-4687

Organisational unit

01159 - Lehre Maschinenbau und Verfahrenstechnik

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Publications 1 - 10 of 22

H∞ Optimal controller design with closed-loop positive real constraints
Hewing, Lukas; Leonhardt, Steffen; Apkarian, Pierre; et al. (2017)
Journal of Dynamic Systems, Measurement, and Control
An Approximate Dynamic Programming approach for Dual Stochastic Model Predictive Control
Arcari, Elena; Hewing, Lukas; Zeilinger, Melanie N. (2020)
IFAC-PapersOnLine ~ 21st IFAC World Congress
Dual control explicitly addresses the problem of trading off active exploration and exploitation in the optimal control of partially unknown systems. While the problem can be cast in the framework of stochastic dynamic programming, exact solutions are only tractable for discrete state and action spaces of very small dimension due to a series of nested minimization and expectation operations. We propose an approximate dual control method for systems with continuous state and input domain based on a rollout dynamic programming approach, splitting the control horizon into a dual and an exploitation part. The dual part is approximated using a scenario tree generated by sampling the process noise and the unknown system parameters, for which the underlying distribution is updated via Bayesian estimation along the horizon. In the exploitation part, we fix the resulting parameter estimate of each scenario branch and compute an open-loop control sequence for the remainder of the horizon. The key benefit of the proposed sampling-based approximation is that it enables the formulation as one optimization problem that computes a collection of control sequences over the scenario tree, leading to a dual model predictive control formulation.
Closed-loop positive real optimal control of variable stiffness actuators
Misgeld, Berno J.E.; Hewing, Lukas; Liu, Lin; et al. (2019)
Control Engineering Practice
Robust gain-scheduled control of variable stiffness actuators
Misgeld, Berno J.E.; Hewing, Lukas; Liu, Lin; et al. (2017)
IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
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 for exogenous high frequency excitation inputs and contributes towards shock resistance and safety in human-robot interaction scenarios. However, the variable element complicates the design of torque and impedance controllers which have to be synthesized by employing contradicting design objectives, such as minimisation of the output impedance, robust stability and performance. Moreover, the system to be controlled consists of an additional control-loop to set-up the stiffness of the elastic element in real-time. To overcome these synthesis problems, we present a new controller design procedure that imposes a positive-real constraint on the load output port function to guarantee a stable interaction with respect to a passive, yet otherwise unknown environments. Additional design requirements are subsequently cast into a generalised plant. Ultimately, a H∞ - nonsmooth design procedure is employed to design a torque controller under these constraints and is tested in in silico experiments with the Mechanical Rotational Impedance Actuator (MeRIA).
Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm
Carron, Andrea; Arcari, Elena; Wermelinger, Martin; et al. (2019)
IEEE Robotics and Automation Letters ~ IEEE Robotics and Automation Letters
On a Correspondence between Probabilistic and Robust Invariant Sets for Linear Systems
Hewing, Lukas; Carron, Andrea; Wabersich, Kim; et al. (2018)
2018 European Control Conference (ECC)
Probabilistic model predictive safety certification for learning-based control
Wabersich, Kim P.; Hewing, Lukas; Carron, Andrea; et al. (2022)
IEEE Transactions on Automatic Control
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 closed-loop behavior in order to meet safety specifications in the presence of physical constraints. This paper introduces a concept called probabilistic model predictive safety certification (PMPSC), which can be combined with any RL algorithm and provides provable safety certificates in terms of state and input chance constraints for potentially large-scale systems. The certificate is realized through a stochastic tube that safely connects the current system state with a terminal set of states that is known to be safe. A novel formulation allows a recursively feasible real-time computation of such probabilistic tubes, despite the presence of possibly unbounded disturbances. A design procedure for PMPSC relying on Bayesian inference and recent advances in probabilistic set invariance is presented. Using a numerical car simulation, the method and its design procedure are illustrated by enhancing an RL algorithm with safety certificates.
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Arcari, Elena; Hewing, Lukas; Schlichting, Max; et al. (2020)
Proceedings of Machine Learning Research ~ Proceedings of the 2nd Conference on Learning for Dynamics and Control
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 into account in the dual control problem, for which the exact solution is provided by stochastic dynamic programming. Due to its computational intractability, we propose a sampling-based approximation for systems affected by both parametric and structural model uncertainty. The approach proposed in this paper separates the prediction horizon in a dual and an exploitation part. The dual part is formulated as a scenario tree that actively discriminates among a set of potential models while learning unknown parameters. In the exploitation part, achieved information is fixed for each scenario, and open-loop control sequences are computed for the remainder of the horizon. As a result, we solve one optimization problem over a collection of control sequences for the entire horizon, explicitly considering the knowledge gained in each scenario, leading to a dual model predictive control formulation.
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Muntwiler, Simon; Wabersich, Kim P.; Hewing, Lukas; et al. (2021)
2021 European Control Conference (ECC)
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 optimization methods. In this work, we propose a distributed stochastic model predictive control (DSMPC) scheme for dynamically coupled linear discrete-time systems subject to unbounded additive disturbances that are potentially correlated in time. An indirect feedback formulation ensures recursive feasibility of the DSMPC problem, and a data-driven, distributed and optimization-free constraint tightening approach allows for exact satisfaction of chance constraints during closed-loop control, addressing typical sources of conservatism. The computational complexity of the proposed controller is similar to nominal distributed MPC. The approach is demonstrated in simulation for the temperature control of a large-scale data center subject to randomly varying computational loads.
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, Lukas; Wabersich, Kim P.; Menner, Marcel; et al. (2020)
Annual Review of Control, Robotics, and Autonomous Systems

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Lukas Hewing

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