3 Search Results for "Rauh, Andreas"

We introduce higher-dimensional automata for infinite interval ipomsets (ω-HDAs). We define key concepts from different points of view, inspired from their finite counterparts. Then we explore languages recognized by ω-HDAs under Büchi and Muller semantics. We show that Muller acceptance is more expressive than Büchi acceptance and, in contrast to the finite case, both semantics do not yield languages closed under subsumption. Then, we adapt the original rational operations to deal with ω-HDAs and show that while languages of ω-HDAs are ω-rational, not all ω-rational languages can be expressed by ω-HDAs.

Recently, we presented an implementation of interval-based algorithms which can be applied in real-time to control dynamical processes and to estimate internal states and disturbances. The approach is based on verified methods for sets of algebraic equations, ordinary differential equations as well as differential-algebraic equations. Due to this fact, the same program code can be used for two different tasks. On the one hand, we can use it online to estimate non-measurable internal system states which are necessary for nonlinear model-based control strategies. On the other hand, we can verify the admissibility and feasibility of these control strategies offline. Although we use the same code for the online and offline tasks, there is an important difference between them. While the computing time is of minor importance in offline applications, we have to guarantee that the necessary online computations are completed successfully in a predefined time interval. For that reason, the role of verification is slightly different depending on the task. In offline applications, our goal is to compute tightest possible bounds for the sets of all solutions to the control problem under consideration. In contrast to that, we restrict the online mode to a search for a single solution that matches all demands on feasibility of control inputs and admissibility of the trajectories of the state variables in a reliable way. To highlight the practical applicability of the underlying computational routines, we present the following cases for the use of verified solvers in real-time [1-3]. Case 1: Direct computation of feedforward control strategies with the help of differential-algebraic equation solvers. In this application, both verified and non-verified solvers can be used to determine open-loop control strategies for a dynamical system such that its output coincides with a predefined time response within given tolerances. This procedure corresponds to a numerical inversion of the dynamics of the system to be controlled. In this case, verified solvers are used to prove the existence of a control law within given physical bounds for the admissible range of the system inputs. Case 2: If measured data and their time derivatives are available, the same procedures as in case 1 can be used to estimate non-measured state variables as well as non-measurable disturbances. Since the verified algorithms used in this context are capable of propagating bounded measurement uncertainties, the quality of the state and disturbance estimates can be expressed in terms of the resulting interval widths. Moreover, assumptions about the parameters and the structure of the underlying model can be verified. Case 3: Routines for verified sensitivity analysis provide further information on the influence of variations of control inputs on the trajectories of the state variables. We present novel procedures implementing a sensitivity-based framework for model-predictive control. These procedures can be integrated directly in a feedback control structure. Sometimes it is necessary to combine verified and non-verified algorithms to solve a given control problem. In this case, it is important to certify the results of the algorithm appropriately. Based on the four-tier hierarchy presented in earlier works [4], we develop a measure for characterizing such mixed approaches. The presentation is concluded with simulation and experimental results for the example of temperature control of a distributed heating system. [1] Rauh, Andreas; Auer, Ekaterina: Applications of Verified DAE Solvers in Engineering, Intl. Workshop on Verified Computations and Related Topics, COE Lecture Note Vol. 15: Kyushu University, pp. 88-96, Karlsruhe, Germany, 2009. [2] Rauh, Andreas; Menn, Ingolf; Aschemann, Harald: Robust Control with State and Disturbance Estimation for Distributed Parameter Systems, Proc. of 15th Intl. Workshop on Dynamics and Control 2009, pp. 135-142, Tossa de Mar, Spain, 2009. [3] Rauh, Andreas; Auer, Ekaterina; Aschemann, Harald: Real-Time Application of Interval Methods for Robust Control of Dynamical Systems, CD-Proc. of IEEE Intl. Conference on Methods and Models in Automation and Robotics MMAR 2009, Miedzyzdroje, Poland, 2009. [4] Auer, Ekaterina; Luther, Wolfram: Numerical Verification Assessment in Computational Biomechanics, in A. Cuyt, W. Krämer, W. Luther, P. Markstein: Numerical Validation in Current Hardware Architectures, LNCS 5492, pp. 145-160, Springer-Verlag, Berlin, Heidelberg, 2009.

Modern techniques for the design and analysis of control strategies for nonlinear dynamical systems are often based on the simulation of the open-loop as well as the closed-loop dynamical behavior of suitable mathematical models. In control engineering, continuous-time and discrete-time state-space representations are widely used which are given by sets of ordinary differential equations and difference equations, respectively. In addition to these representations, sets of differential algebraic equations are commonly used. Since we will focus on computational techniques which are applied for the design and mathematical verification of controllers for lumped parameter systems, i.e., systems which do not contain elements with distributed parameters, partial differential equations will not be considered in this talk. The prerequisite for the design and robustness analysis of each control system is the identification of mathematical models which describe the dynamics of the plant to be controlled as well as the available measurement devices with a sufficient accuracy. The model identification task comprises the derivation of physically motivated state equations, their parameterization based on measured data, as well as simplifications to apply specific approaches for controller design. In the design stage, both open-loop and closed-loop control strategies can be considered. Since dynamical system models are subject to uncertain parameters and uncertain initial conditions in most practical applications, detailed mathematical formulations of the desired dynamics of the controlled system are necessary. These specifications involve the definition of robustness with respect to the above-mentioned uncertainties. For linear system representations, robustness is commonly specified in terms of regions in the complex domain containing all admissible poles of the closed-loop transfer functions ($Gamma$-stability) or in terms of specifications of worst-case bounds for the frequency response ($mathcal{B}$-stability) [1]. However, these specifications do not allow for inclusion of bounds for the state variables which are often available in the time domain if controllers are designed for safety critical applications. Especially for nonlinear dynamical systems, pole assignment based on the linearization of nonlinear mathematical models generally leads to the necessity for the analysis of asymptotic stability of the resulting closed-loop dynamics. In this presentation, we will give an overview of the potential use of validated techniques for the analysis and design of controllers for nonlinear dynamical systems with uncertainties, where the systems under consideration will be subject to constraints for both state and control variables. As an application scenario the design of robust control strategies for a biological wastewater treatment process will be discussed. In the design and the verification process, constraints for both state and control variables which are given by guaranteed interval bounds in the time domain are taken into account. Suitable computational techniques are, for example, based on an extension of the validated initial value problem solver {sc ValEncIA-IVP} [2,6]. For that purpose, differential sensitivities of the trajectories of all state variables with respect to variations of the parameters of the mathematical system model as well as the adaptation of controller parameters are computed. This information can then be used for online identification and adaptation of parameters during the operation of a closed-loop controller as well as in offline design, verification, and optimization. Here, the interval arithmetic routines for sensitivity analysis allow to compute guaranteed differential sensitivity measures for system models with both nominal parameters and interval uncertainties. The presented interval arithmetic techniques are the basis for a general purpose tool for the analysis and the design of robust and optimal control strategies for uncertain dynamical systems. The presentation is concluded with an outlook on the formulation of control problems using sets of differential algebraic equations. Possibilities for the extension of {sc ValEncIA-IVP} to this type of system representation will be summarized. Relations between the presented interval arithmetic approach and methods for stabilizing control of nonlinear dynamical systems which make use of structural system properties such as differential flatness [3] and exact feedback linearization are highlighted [4,5]. In the latter case, input-output linearization as well as (in special cases) input-to-state linearization are of practical importance. References: [1] J. Ackermann, P. Blue, T. B"unte, L. G"uvenc, D. Kaesbauer, M. Kordt, M. Muhler, and D. Odenthal, {it{Robust Control: The Parameter Space Approach}}, Springer--Verlag, London, 2nd edition, 2002. [2] E. Auer, A. Rauh, E. P. Hofer, and W. Luther, {it{Validated Modeling of Mechanical Systems with {sc SmartMOBILE}: Improvement of Performance by {sc ValEncIA-IVP}}}, In Proceedings of Dagstuhl Seminar 06021: Reliable Implementation of Real Number Algorithms: Theory and Practice, Lecture Notes in Computer Science, Dagstuhl, Germany, 2006. In print. [3] M. Fliess, J. Lévine, P. Martin, and P. Rouchon, {it{Flatness and Defect of Nonlinear Systems: Introductory Theory and Examples}}, International Journal of Control, vol. 61, pp. 1327--1361, 1995. [4] H. K. Khalil, {it{Nonlinear Systems}}, Prentice-Hall, Upper Saddle River, New Jersey, 3rd edition, 2002. [5] H. J. Marquez, {it{Nonlinear Control Systems}}, John Wiley & Sons, Inc., New Jersey, 2003. [6] A. Rauh and E. Auer, {{www.valencia-ivp.com}}.