Andreas Kroll

Robust adaptive fault detection using global state information and application to mobile working machines

In this paper, an observer-based fault detection approach for a class of nonlinear systems is presented, which can be modeled by Takagi-Sugeno (TS) fuzzy models. We propose a sliding mode fuzzy observer that deals with bounded uncertainties in the plant and allows fault estimation based on an equivalent output error injection approach [3]. Furthermore an adaption scheme based on pattern recognition algorithms is presented. It allows to deal with situational uncertainties, which affect the system, by adapting the fault sensitivity. An extensive simulation of a mobile working machine is used to demonstrate the effectiveness of the proposed scheme.

Design of sliding mode observers for TS fuzzy systems with application to disturbance and actuator fault estimation

In this paper an observer-based fault detection approach for a class of nonlinear systems, which can be modeled by Takagi-Sugeno (TS) fuzzy models, is presented. We propose a sliding mode fuzzy observer that deals with unmeasurable premise variables, bounded uncertainties in the plant and allows fault estimation based on an equivalent output error injection approach. Necessary conditions for the existence of the robust observer are derived from sliding mode theory [10]. Stability is ensured by linear matrix inequality (LMI) based sufficient conditions. An illustrative example demonstrates the effectiveness of the proposed scheme.

On autonomous detection of pressured air and gas leaks using passive IR-thermography for mobile robot application

Today, pressured air and gas leaks are typically detected using in-situ sensor technology. In this contribution the application of passive IR-thermography is proposed to permit remote leak detection by assessing the resulting temperature profile disturbance due to expansion of pressured gas. Remote measurements are advantageous as they are easier and safer to conduct. Scanning high-rise objects can be achieved using a ground-bound system without requiring complex climbing. In the paper a novel method for automated leak detection by feature extraction and pattern recognition is presented. This enables autonomous mobile robots with remote leak detection capability.

Nonlinear Black Box Modelling – Fuzzy Networks versus Neural Networks

Fuzzy networks and neural networks offer two different approaches of nonlinear black box modelling. Efficient identification methods have been developed to calculate the parameters for a given structure and have been applied successfully in many examples. But the applications proposed in the literature usually miss the comparison of the alternative method, so that the selection of the more suitable approach for a given task is difficult. This paper aims to ease the decision for one of the two methodologies by considering one well-known high quality approximator of each network type, and presenting a fair comparison. For this purpose, two mathematical and three complex technical examples of nonlinear systems are considered. Generally, fuzzy networks and neural networks face the problem of overtraining causing poor validation/generalisation results. A modification of the established identification methods is proposed as a significant improvement for both approaches.

Benchmark problems for nonlinear system identification and control using Soft Computing methods

HighlightsCollection of 13 benchmark problems described in detail in standardized way.General assessment criteria as well as problem-specific tests specified.Benchmarks span from simple artificial systems to complex entire industrial plants.Many domains covered incl. drives, mechatronics, chemical plants, wind turbines.Examples of use in Soft Computing community are provided for each problem. Using benchmark problems to demonstrate and compare novel methods to the work of others could be more widely adopted by the Soft Computing community. This article contains a collection of several benchmark problems in nonlinear control and system identification, which are presented in a standardized format. Each problem is augmented by examples where it has been adopted for comparison. The selected examples range from component to plant level problems and originate mainly from the areas of mechatronics/drives and process systems. The authors hope that this overview contributes to a better adoption of benchmarking in method development, test and demonstration.

A centralized multi-robot task allocation for industrial plant inspection by using a* and genetic algorithms

Multi-robot systems are widely employed in various applications including industrial plant inspection. However, current research work mainly focuses on the methods of object detection, and rarely addresses task allocation and path-planning of multi-robot systems for industrial plant inspection. Therefore, a centralized method for multi-robot task allocation (MRTA) and path-planning to solve inspection problems is proposed in this paper. For the first time, the problem statement of the task allocation for inspection problems is formulated. This paper introduces the implementation of the algorithm based on A* and a novel Genetic Algorithm including the environment representation. The task allocation and path-planning are performed based on the assumption that the robots work in known environments. The proposed algorithm is tested in a simulation study derived from a large industrial site.

Mobile robots with active IR-optical sensing for remote gas detection and source localization

While other robots use in-situ measurements for gas leak detection and localization, we propose to apply remote sensing. It is easier and safer to conduct, permits rapid scans and is applicable to leak sources high up. An IR-optical sensor is used, exploiting spectral absorption effects of gases. Tailored leak detection and localization strategies are proposed. A simulation environment with a 3D model of the gas concentration field is used for developing and testing the detection and localization strategies. The system performance is demonstrated in a case study with a chemical plant.

Memetic algorithms for optimal task allocation in multi-robot systems for inspection problems with cooperative tasks

Multi-robot task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize cost while satisfying operational constraints. It can be challenging to solve a large number of tasks and becomes even more challenging when tightly coupled multi-robot tasks are also taken into account. For example, it is more complex to solve problems that include tasks that have to be carried out jointly by two robots due to the resulting temporal and spatial constraints. Additionally, the complexity of task allocation increases exponentially with rising task variety. This paper focuses on multi-robot task allocation in inspection problems with both single- and two-robot tasks. A novel memetic algorithm is proposed combining a genetic algorithm with two local search schemes. Using permutation representation, eight approaches based on four basic coding strategies are designed for multi-robot task allocation of inspection problems with two-robot tasks. The performance of the memetic algorithm is evaluated in case studies on inspecting a large storage tank area of a petroleum refinery.

Gas leak localization in industrial environments using a TDLAS-based remote gas sensor and autonomous mobile robot with the Tri-Max method

In order to automate routine inspections in large industrial environments, a mobile robotic system is being developed in the RoboGasInspector project. The robots sensor-head consists of different instruments for remote gas sensing using multiple measurement principles. This contribution focuses on Tunable Diode Laser Absorption Spectroscopy (TDLAS). The measurement device allows quantitative gas concentration measurements and achieves high sensitivity due to the active measurement principle. On the contrary, TDLAS systems measure the concentration along a path at a time and require a diffusely reflecting background. Using a pan-tilt unit, objects and areas can be scanned. This paper describes the triangulation-maximization leak-localizing strategy (Tri-Max) used in the project RoboGasInspector. Results based on outdoor experiments in two industrial plants and a landfill dump are presented.

Fuzzy network model-based fuzzy state controller design

The performance of model-based controller design relies heavily on the quality and suitability of the utilized process model. This contribution proposes a fuzzy network based nonlinear controller design methodology. Fuzzy networks are a model approach combining high approximation quality with high interpretability. The input/output (I/O) models commonly used for identification are transformed to fuzzy state-space models. Transferring and adjusting methods from linear state-space theory a control concept consisting of a fuzzy state controller and an adaptive set-point filter for nonlinear dynamic processes is deduced. The capability of the method is demonstrated for a hydraulic drive.