S. Hirche

Technische Universität München, München, Bavaria, Germany

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Publications (64)27.59 Total impact

  • M.H. Mamduhi · D. Tolic · S. Hirche
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    ABSTRACT: This paper modifies a recently proposed event-based probabilistic medium access for multi-loop Networked Control Systems (NCSs) over a shared communication channel subject to limited capacity and uncertainties and study its robustness. The novel design combines deterministic and probabilistic attributes to efficiently allocate the channel access among the control loops in the presence of network-induced phenomena such as packet dropouts and scheduling with delayed information update. Since the scheduler receives error information from a number of systems simultaneously, this sheer amount of information cannot always be processed in timely manner, which in turn gives rise to delays. Given the local error thresholds, the subsystems with error values lower than their corresponding thresholds are deterministically excluded from the medium access competition in favor of those with larger errors. In case of resource scarcity, the scheduler probabilistically allocates the channel to those that exceed the local thresholds according to an error-dependent priority measure. We show stochastic stability of such NCSs in terms of '-ergodicity of the network-induced error, which is modeled as a Markov chain. Numerical results validate our stability results in the presence of packet dropouts and delayed data update.
    No preview · Article · Jul 2015 · Proceedings of the American Control Conference
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    ABSTRACT: This paper discusses an online dynamic motion generation scheme for nonprehensile object manipulation by using a set of predefined motions and a trajectory deformation algorithm capable of incorporating positional and velocity boundary constraints. By creating optimal trajectories offline and deforming them online, computational complexity during execution is reduced considerably. As tight convex hulls of the deformed trajectories can be found, possible obstacles or workspace boundaries can be circumnavigated precisely without collision. The approach is verified through experiments on an inclined planar air-table for volleyball scenario using two 3-DoF robots.
    No preview · Article · Jun 2015 · Proceedings - IEEE International Conference on Robotics and Automation
  • F. Deroo · S. Hirche · B.D.O. Anderson
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    ABSTRACT: In this paper we analyze the propagation of input signals in a large-scale network of dynamical systems. Using vector Lyapunov functions, the individual multidimensional subsystems are first reduced to an approximating scalar representation in the form of the evolution of their weighted norm. The norm simultaneously qualifies as a local Lyapunov function for the isolated subsystem. Employing properties of M-matrices, we then derive linear system dynamics which provide an upper bound for the evolution of the original system, and use them to investigate the decay between hops from subsystem to subsystem of the steady state magnitude. Two input signals are considered: Constant input, and a sinusoidal input. The results are demonstrated using numerical simulations.
    No preview · Article · Feb 2015
  • D. Tolic · S. Hirche
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    ABSTRACT: This paper proposes a methodology for computing Maximally Allowable Transfer Intervals (MATIs) that provably stabilize nonlinear Networked Control Systems (NCSs) in the presence of disturbances and signal delays. Accordingly, given a desired level of system performance (in terms of p-gains), quantitative MATI vs. delay trade-offs are obtained. By combining impulsive delayed system modeling with Lyapunov-Razumikhin type of arguments, we are able to consider even the so-called large delays. Namely, the computed MATIs can be smaller than delays existent in NCSs. In addition, our stability results are provided for the class of Uniformly Globally Exponentially Stable (UGES) scheduling protocols. The well-known Round Robin (RR) and Transmit-Once-Discard (TOD) protocols are examples of UGES protocols. Apart from the inclusion of large delays, another salient feature of our methodology is the consideration of corrupted data. To that end, we propose the notion of p-stability with bias. Furthermore, the Zeno-free property of our methodology is demonstrated. Finally, a comparison with the state-of-the-art work is provided utilizing the benchmark problem of batch reactor.
    No preview · Article · Feb 2015 · Proceedings of the IEEE Conference on Decision and Control
  • M.H. Mamduhi · D. Tolic · A. Molin · S. Hirche
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    ABSTRACT: In this paper, we study event-triggered data scheduling for stochastic multi-loop control systems communicating over a shared network with communication uncertainties. We introduce a novel dynamic scheduling scheme which allocates the channel access according to an error-dependent policy. The proposed scheduler deterministically excludes subsystems with lower error values from the medium access competition in favor of those with larger errors. Subsequently, the scheduler probabilistically allocates the communication resource to the eligible entities. We model the overall network-induced error as a homogeneous Markov chain and show its boundedness in expectation over a multi time-step horizon. In addition, analytical upper bound for the associated average cost is derived. Furthermore, we show that our proposed policy is robust against packet dropouts. Numerical results demonstrate a significant performance improvement in terms of error level in comparison with periodic and random scheduling policies.
    No preview · Article · Feb 2015 · Proceedings of the IEEE Conference on Decision and Control
  • M. Kimmel · S. Hirche
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    ABSTRACT: In many control applications, state and/or output constraints need to be satisfied. For that purpose, artificial limits are imposed using a specially designed control law. Among other methods, invariance control has proven valuable for addressing the problem of state and output constraints in nonlinear control systems. However, invariance controlled systems often exhibit undesirable chattering behavior in particular in digital implementation. In this work, we propose a novel invariance-based control approach, which significantly reduces chattering. We give a condition for stability and restrictions on the admissible constraint configuration. The approach and the results are illustrated in simulations.
    No preview · Conference Paper · Dec 2014
  • T. Nierhoff · S. Hirche · Y. Nakamura
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    ABSTRACT: This paper presents an incremental sampling-based approach for trajectory imitation in cluttered environments using the RRT∗ algorithm. Inspired by the discrete Laplace-Beltrami operator the underlying distance metric is based upon the difference from a reference trajectory through a quadratic distance term incorporating velocity and acceleration deviations along the trajectory. Mathematically-backed approximations in combination with a task-space bias make it possible to use standard nearest neighbor methods in task space when expanding the RRT∗-tree. It is shown that metric-consistent biases considerably increase the convergence speed. The proposed approach is validated in simulations in a 2D environment and in experiments using a HRP-4 humanoid robot.
    No preview · Article · Oct 2014
  • I. Palunko · P. Donner · M. Buss · S. Hirche
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    ABSTRACT: Cooperative dynamic object manipulation can extend the manipulation capabilities of robot-robot and human-robot teams. In order to be able to inject energy into various suspended objects of unknown parameters, in this paper we propose an adaptive controller which combines reinforcement learning with energy based swing-up control. The proposed controller is successfully verified in a single robot and human-robot experimental setup for different types of suspended objects.
    No preview · Article · Oct 2014
  • Source
    Adam Molin · Sandra Hirche
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    ABSTRACT: The advent of networked control systems urges the digital control design to incorporate communication constraints efficiently. In order to accommodate this requirement, this article studies the joint design of controller and event-trigger for linear stochastic systems in the presence of a resource-limited communication channel which exhibits packet dropouts and time-delay. The event-trigger situated at the sensor decides at every sampling instance, whether to send information over the communication channel to the controller. The design approach is formulated as a stochastic average-cost optimization problem, where the communication constraints are reflected as an additional cost penalty of the average transmission rate. Different conditions on the communication model are given where the joint optimal design can be split into a separate control and event-trigger design. Based on these results, two suboptimal design approaches are developed. By using drift criteria, stability guarantees of the closed-loop system for both approaches are derived in terms of bounded moment stability. Numerical simulations illustrate the efficacy of the event-triggered approach compared with optimal time-triggered controllers.
    Full-text · Article · Apr 2014 · ZAMM Journal of applied mathematics and mechanics: Zeitschrift für angewandte Mathematik und Mechanik
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    ABSTRACT: For interconnected systems, the state estimation and control algorithms associated with the subsystems can communicate over the network to improve their performance. This chapter describes methods for structuring the overall control system including the communication network and shows how to use the communicated information for estimation and control if the network induces transmission delays and packet loss.
    No preview · Article · Jan 2014
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    ABSTRACT: Event-based control is a control methodology that is currently being developed as a means to reduce the communication between the sensors, the controller and the actuators in a control loop. The sampling instants are not determined periodically by a clock, but by an event generator, which adapts the information flow in the feedback loop to the current behavior of the closed-loop system. A communication among the components is invoked only after an event has indicated that the control error exceeds a tolerable bound.
    No preview · Article · Jan 2014
  • F. Deroo · M. Meinel · M. Ulbrich · S. Hirche
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    ABSTRACT: Most results on distributed control design of large-scale interconnected systems assume a central designer with global model knowledge. The wish for privacy of subsystem model data raises the desire to find control design methods to determine an optimal control law without centralized model knowledge, i.e. in a distributed fashion. In this paper we present a distributed control design method with guaranteed stability to minimize an infinite horizon LQ cost functional. The introduction of adjoint states allows to iteratively optimize the feedback matrix using a gradient descent method in a distributed way, based on a finite horizon formulation. Inspired by ideas on stabilizing model predictive control, a terminal cost term is used, which gives a bound on the infinite horizon cost functional and ensures stability. A method is presented to determine that term in a distributed fashion. The results are validated using numerical experiments.
    No preview · Article · Jan 2014
  • T. Nierhoff · S. Hirche · Y. Nakamura
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    ABSTRACT: Vector field methods for trajectory following are generally computed offline before execution and thus only applicable to static trajectories. In contrast this paper introduces Laplacian trajectory vector fields (LTVF) as a computationally efficient method for creating convergent vector fields towards a discretized reference trajectory. In case of environmental changes both the vector field and the reference trajectory can be quickly recomputed. The conducted experiment uses a HRP-4 robot in order to display the applicability to daily life problems.
    No preview · Article · Jan 2014
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    ABSTRACT: It is well-known that physical robotic assistance to humans is significantly enhanced by including human behavior anticipation into robot planning and control. The challenge arises when the human goal/plan is uncertain or unknown to the robot. In this paper we propose a novel control scheme which dynamically selects between a model-based and a model-free strategy depending on the level of disagreement between the human and the robot. The disagreement is measured in terms of the interaction force. A task specific model-based controller is selected when the human's motion intention coincides with the robot's goal. A model-free control scheme based on the human force as motion prediction source is selected in case of disagreement and when the human goal/plan is unknown. The benefits of this approach are demonstrated in a human user study on human-robot cooperative object transport through a 2D maze in virtual reality.
    Full-text · Conference Paper · May 2013
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    Jose Ramon Medina · Dominik Sieber · Sandra Hirche
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    ABSTRACT: Manipulation tasks are a great challenge for robots due to the uncertainty arising from unstructured environments. In this paper we propose a novel control scheme for contact tasks based on risk-sensitive optimal feedback control. It provides a systematic approach to adjust the trade-off between motion and force control under uncertainty. Following a previously acquired task model, the proposed approach provides both a variable stiffness solution and a motion reference adaptation. This control scheme achieves increased adaptability under previously unseen environmental variability. An implementation on a robotic manipulator validates the applicability and adaptability of the proposed control approach in two different manipulation tasks.
    Full-text · Conference Paper · May 2013
  • Tamara Lorenz · Alexander Mortl · Sandra Hirche
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    ABSTRACT: Interpersonal movement synchronization is a phenomenon that does not only increase the predictability of movements; it also increases rapport among people. In this line, synchronization might enhance human-robot interaction. An experiment is presented which explores to what extend a human synchronizes own movements to a non-adaptive robot during a repetitive tapping task. It is shown that the human does not take over the complete effort of movement adaptation to reach synchronization, which indicates the need for adaptive robots.
    No preview · Conference Paper · Mar 2013
  • Haiyan Wu · Lei Lou · Chih-Chung Chen · Sandra Hirche · Kolja Kuhnlenz
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    ABSTRACT: The performance of vision-based control systems, in particular, of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control (NVSC), which integrates networked computational resources for cloud image processing, is considered in this paper. The main contributions of this paper are the following: 1) a real-time transport protocol for transmitting large-volume image data on a cloud-computing platform, which enables high-sampling-rate visual feedback; 2) a stabilizing control law for the NVSC system with time-varying feedback time delay; and 3) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the NVSC system with sending rate scheduling is validated in an object-tracking scenario on a 14-DOF dual-arm robot. Experimental results show the superior performance of our approach. In particular, the communication network load is substantially reduced by means of the scheduling strategy without performance degradation.
    No preview · Article · Feb 2013 · IEEE Transactions on Industrial Electronics
  • Source
    Adam Molin · Sandra Hirche
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    ABSTRACT: Digital control design is commonly constrained to time-triggered control systems with periodic sampling. The emergence of more and more complex and distributed systems urges the development of advanced triggering schemes that utilize communication, computation, and energy resources more efficiently. This technical note addresses the question whether certainty equivalence is optimal for an event-triggered control system with resource constraints. The problem setting is an extension of the stochastic linear quadratic system framework, where the joint design of the control law and the event-triggering law minimizing a common objective is considered. Three differing variants are studied that reflect the resource constraints: a penalty term to acquire the resource, a limitation on the number of resource acquisitions, and a constraint on the average number of resource acquisitions. By reformulating the underlying optimization problem, a characterization of the optimal control law is possible. This characterization shows that the certainty equivalence controller is optimal for all three optimization problems.
    Preview · Article · Feb 2013 · IEEE Transactions on Automatic Control
  • M. Lawitzky · M. Kimmel · P. Ritzer · S. Hirche
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    ABSTRACT: Trajectory generation for active physical assistance to humans in cooperative haptic tasks gains increasing interest in recent literature. Planning-based approaches represent one class of trajectory synthesis methods for active robotic partners. To overcome the limitations of kinematic planning algorithms in dynamic tasks, we propose a three-step approach to the synthesis of trajectories under the principle of least action. This is motivated by neuroscientific findings on human effort minimization in motor tasks. A trajectory is generated by optimized sequencing of optimal motion primitives. The benefits of the proposed method for physical human-robot cooperation are demonstrated in human user studies in a 2D cooperative transport task in a virtual maze.
    No preview · Conference Paper · Jan 2013
  • Adam Molin · Sandra Hirche
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    ABSTRACT: Under many circumstances event-triggered control outperforms time-triggered control schemes when resources such as communication, computation, or energy are sparse. This paper investigates another benefit of event-triggered control concerning the ability of adaptation. The system under consideration comprises multiple heterogeneous control loops that are closed over a shared communication network. Each subsystem is modelled as a discrete-time stochastic linear system. The design problem is formulated as an average cost Markov decision process (MDP) with unknown global system parameters that are to be estimated during execution. Techniques from distributed optimization and adaptive MDPs are used to develop distributed self-regulating event-triggers that adjust their transmission rate to meet a global resource constraint. Numerical simulations show the effectiveness of the approach and illustrate the convergence properties.
    No preview · Conference Paper · Dec 2012

Publication Stats

869 Citations
27.59 Total Impact Points

Institutions

  • 2005-2014
    • Technische Universität München
      • • Department of Information-oriented Control
      • • Institute of Automatic Control Engineering (LSR)
      München, Bavaria, Germany
  • 2006-2011
    • Tokyo Institute of Technology
      • Department of Department of Mechanical and Control Engineering
      Edo, Tōkyō, Japan
  • 2003
    • Technische Universität Berlin
      • School IV Electrical Engineering and Computer Science
      Berlin, Land Berlin, Germany