Verified integration of differential equations with discrete delay

Many dynamic system models in population dynamics, physics and control involve temporally delayed state information in such a way that the evolution of future state trajectories depends not only on the current state as the initial condition but also on some previous state. In technical systems, such phenomena result, for example, from mass transport of incompressible fluids through finitely long pipelines, the transport of combustible material such as coal in power plants via conveyor belts, or information processing delays. Under the assumption of continuous dynamics, the corresponding delays can be treated either as constant and fixed, as uncertain but bounded and fixed, or even as state-dependent. In this paper, we restrict the discussion to the first two classes and provide suggestions on how interval-based verified approaches to solving ordinary differential equations can be extended to encompass such delay differential equations. Three close-to-life examples illustrate the theory

Toward the development of iteration procedures for the interval-based simulation of fractional-order systems

In many fields of engineering as well as computational physics, it is necessary to describe dynamic phenomena which are characterized by an infinitely long horizon of past state values. This infinite horizon of past data then influences the evolution of future state trajectories. Such phenomena can be characterized effectively by means of fractional-order differential equations. In contrast to classical linear ordinary differential equations, linear fractional-order models have frequency domain characteristics with amplitude responses that deviate from the classical integer multiples of ±20 dB per frequency decade and, respectively, deviate from integer multiples of ± 2 in the limit values of their corresponding phase response. Although numerous simulation approaches have been developed in recent years for the numerical evaluation of fractional-order models with point-valued initial conditions and parameters, the robustness analysis of such system representations is still a widely open area of research. This statement is especially true if interval uncertainty is considered with respect to initial states and parameters. Therefore, this paper summarizes the current state-of-the-art concerning the simulation-based analysis of fractional-order dynamics with a restriction to those approaches that can be extended to set-valued (interval) evaluations for models with bounded uncertainty. Especially, it is shown how verified simulation techniques for integer-order models with uncertain parameters can be extended toward fractional counterparts. Selected linear as well as nonlinear illustrating examples conclude this paper to visualize algorithmic properties of the suggested interval-based simulation methodology and point out directions of ongoing research

Validated Model Predictive Control based on Exponential Enclosures

[no abstract available

Cooperativity and its use in robust control and state estimation for uncertain dynamic systems with engineering applications

This work shows a general applicable approach to robustly control uncertain dynamic systems, where the uncertainty is given by bounded intervals. The presented robust control methods rely on a verified enclosure of the state intervals. Since state-of-the-art-methods to calculate this fail, the property of cooperativity is used. However, since not all systems are naturally cooperative, a transformation routine is established to widen the possible application of this method. Different application scenarios chosen from a variety of engineering fields are used to validate the theoretical findings.Diese Arbeit zeigt einen generell verwendbaren Ansatz, um ein unsicheres dynamisches System robust zu regeln. Der gezeigte Ansatz verwendet dabei verifizierte Intervalleinschlüsse, die sich aus der intervallbasierten Unsicherheit ergeben. Da moderne Rechenmethoden hierbei versagen, wird die Eigenschaft der Kooperativität ausgenutzt, um dies dennoch zu ermöglichen. Da nicht alle Systeme diese Eigenschaft direkt aufweisen, wird eine Transformationsroutine entwickelt, um den gezeigten Ansatz auf andere Einsatzszenarien zu erweitern. Dies wird durch verschiedene Anwendungen in der Arbeit bewiesen

Verified interval enclosure techniques for robust gain scheduling controllers

In real-life applications, dynamic systems are often subject to uncertainty due to model simplifications, measurement inaccuracy or approximation errors which can be mapped to specific parameters. Uncertainty in dynamic systems can come either in stochastic forms or as interval representations. The latter is applied if the uncertainty is bounded as it will be done in this paper. The main idea is to find a joint approach for an interval-based gain scheduling controller while simultaneously reducing overestimation by enclosing state intervals with the least amount of conservativity. The robust and/ or optimal control design is realized using linear matrix inequalities (LMIs) to find an efficient solution and aims at a guaranteed stabilization of the system dynamics over a predefined time horizon. A temporal reduction of the widths of intervals representing worst-case bounds of the system states at a specific point of time should occur due to asymptotic stability proven by the employed LMI-based design. However, for commonly used approaches in the computation of interval enclosures, those interval widths seemingly blow up due to the wrapping effect in many cases. To avoid this, we provide two interval enclosure techniques — an exploitation of cooperativity and an exponential approach — and discuss their applicability taking into account two real-life applications, a high-bay rack feeder and an inverse pendulum

Evaluation of the effectiveness of the interval computation method to simulate the dynamic behavior of subdefinite system: application on an active suspension system

International audienceA new design approach based on methods by intervals adapted to the integration of the simulation step at the earliest stage of preliminary design for dynamic systems is proposed in this study. The main idea consists on using the interval computation method to make a simulation by intervals in order to minimize the number of simulations which allow obtaining a set of solutions instead of a single one. These intervals represent the domains of possible values for the design parameters of the subdefinite system. So the parameterized model of the system is solved by interval. This avoids launching n simulations with n values for each design parameter. The proposed method is evaluated by several tests on a scalable numerical example. It has been applied to solve parameterized differential equations of a Macpher-son suspension system and to study its dynamic behavior in its passive and active form. The dynamic model of the active suspension is nonlinear but linearisable. It is transformed into a parameterized state equation by intervals. The solution to this state equation is given in the form of a matrix exponential. Three digital implementations of exponential have been tested to obtain convergent results. Simulations results are presented and discussed