A diagnosis-based causal analysis method for concurrent hybrid automata

Abstract. Modern artifacts are typically composed of many system components and exhibit a complex pattern of continuous/discrete behaviors. A concurrent hybrid automaton is a powerful modeling concept to capture such a system’s behavior in terms of hybrid automata for the individual system components and the concurrent composition of thereof. Because of the potentially large number of modes of the concurrent automaton model it is non-trivial to validate the composition such that every possible operational mode leads to a causally valid dynamic model for the overall system. This paper presents a novel model analysis method that validates the automata composition without the necessity to analyze a prohibitively large number of modes. We achieve this by formulating the exhaustive causal analysis of hybrid automata as a diagnosis problem. This provides causal specifications of the component automata and enables us to efficiently calculate the causal relationships for their concurrent composition and thus validate a concurrent automaton model.

Hybrid estimation of complex systems

Abstract—Modern automated systems evolve both continuously and discretely, and hence require estimation techniques that go well beyond the capability of a typical Kalman Filter. Multiple model (MM) estimation schemes track these system evolutions by applying a bank of filters, one for each discrete system mode. Modern systems, however, are often composed of many interconnected components that exhibit rich behaviors, due to complex, system-wide interactions. Modeling these systems leads to complex stochastic hybrid models that capture the large number of operational and failure modes. This large number of modes makes a typical MM estimation approach infeasible for online estimation. This paper analyzes the shortcomings of MM estimation, and then introduces an alternative hybrid estimation scheme that can efficiently estimate complex systems with large number of modes. It utilizes search techniques from the toolkit of model-based reasoning in order to focus the estimation on the set of most likely modes, without missing symptoms that might be hidden amongst the system noise. In addition, we present a novel approach to hybrid estimation in the presence of unknown behavioral modes. This leads to an overall hybrid estimation scheme for complex systems that robustly copes with unforeseen situations in a degraded, but fail-safe manner. Index Terms—Artificial intelligence, diagnosis, fault detection and isolation (FDI), hybrid systems, multiple model estimation. I

Amplitude model for beam oscillations in the main Linac of CLIC

To achieve the challenging goal of ultra-low emittance preservation in the main linac of CLIC, different techniques are used. The according algorithms often rely on an accurate, fast and efficient to compute model of the amplitude behavior of the beam oscillations in the beam line. In this paper such a model is developed, considering the accelerator design as well as the effect of filamentation. Filamentation is especially important, due to the high energy spread of the according beam and the large total phase advance of the lattice. Therefore a general model to describe filamentation is adapted to the properties of the beam in the main linac of CLIC. At the beginning of the linac, where made assumptions are not valid, this basic model is supported by a fit to simulation data. An accuracy evaluation of the produced data shows that the quadratic error is around 4 %. Therefore, the developed model delivers a fast and efficient procedure, to precisely predict the beam envelope behavior in the main linac of CLIC

SVD-based filter design for the trajectory feedback of CLIC

The trajectory feedback of the Compact Linear Collider (CLIC) is an essential mitigation method for\nground motion effects at CLIC. In this paper significant improvements of the design of this feedback are\npresented. The new controller is based on a singular value decomposition (SVD) of the orbit response\nmatrix to decouple the in- and outputs of the accelerator. For each decoupled channel one independent\ncontroller is designed by utilising ground motion and noise models. This new design allows a relaxation of\nthe required resolution of the beam position monitor from 10 to 50 nm. At the same time the suppression\nof ground motion effects is improved. As a consequence, the tight tolerances for the allowable luminosity\nloss due to ground motion effects in CLIC can be met. The presented methods can be easily adapted to\nother accelerators in order to loosen sensor tolerances and to efficiently suppress ground motion effect

Paper submitted to IJCAI-01 Mode Estimation of Probabilistic Hybrid Systems

Model-based diagnosis and mode estimation capabilities excel at diagnosing systems whose symptoms are clearly distinguished from normal behavior.A strength of mode estimation in particular is its ability to track a system’s discrete dynamics as it moves between different behavioral modes.However, often failures bury their symptoms amongst the signal noise, until their effects become catastrophic. We introduce a hybrid mode estimation system that extracts mode estimates from subtle symptoms.First we introduce a modeling formalism, called probabilistic hybrid automata (PHA), that merge hidden Markov models (HMM) with continuous dynamical system models.Second, we introduce hybrid estimation as a method for tracking and diagnosing PHA, by unifying traditional continuous state observers with HMM belief update.Finally, we introduce a novel, any-time, any-space algorithm for computing approximate hybrid estimates.This approach pursues the most promising estimates, based on a statistical measure of the probability that an estimate will turn out likely.

On-line Kinematics Reasoning for Reconfigurable Robot Drives

Abstract- The control system for a mobile robot typically assumes fixed kinematics according to the drive's geometry and functionality. Faults in the system, for example a blocked steering actuator, will then lead to an undesired behaviour, unless one takes care of specific single and/or multiple faults explicitly. We present a novel model-programmed procedure for on-line kinematics reasoning that allows a robot to deduce the (inverse)-kinematics of the drive and also its kinematic abilities for the specific modes of operation and some falt modes during operation. As a consequence, we can reconfigure a robot drive to compensate for some faults and also inform a higher level control system about changed mobility capabilities of a robot. Being fault tolerant is, however, only one advantage of our approach that derives the kinematics control strategy from a geometric and functional model of the drive. We can easily adapt the controller for various robot drives, handle drives that change their geometry and functionality during run-time and also provide the basis for a flexible control scheme for self-configuring multi-robot systems

Mode set focused hybrid estimation

International audienceEstimating the state of a hybrid system means accounting for the mode of operation or failure and the current state of the continuously valued entities concurrently. Existing hybrid estimation schemes try to overcome the problem of an exponentially growing number of possible mode-sequence/continuous-state combinations by merging hypotheses and/or deducing likelihood measures to identify tractable sets of the most likely hypotheses. However, they still suffer from unnecessarily high computational costs as the number of possible modes increases. Hybrid diagnosis schemes, on the other hand, estimate the current mode of operation/failure only, thus leaving the continuous evolution of the system implicit. This paper proposes a novel scheme that uses a combination of both the approaches in order to define posterior transition probabilities between the specified modes of the hybrid system, hence focusing better on relevant hypotheses. In order to demonstrate the effectiveness of the proposed method, the algorithm is applied to a satellite attitude control system and compared with existing hybrid estimation/diagnosis schemes, such as the Interacting Multiple Model (IMM) algorithm, a purely parity based method (HyDiag), and an existing hybrid Mode Estimation (hME) algorithm

Improving

robustness of mobile robots using model-based reasonin