R-3 Repository :: Browsing by Author "Leuschen, Martin L."

Browsing by Author "Leuschen, Martin L."

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Derivation and Application of Nonlinear Analytical Redundancy Techniques with Applications to Robotics
(2001-11-20) Leuschen, Martin L.; Center for Multimedia Communications (http://cmc.rice.edu/)
Derivation and Application of Nonlinear Analytical Redundancy Techniques with Applications to Robotics Fault detection is important in many robotic applications. Failures of powerful robots, high velocity robots, or robots in hazardous environments are quite capable of causing significant and possibly irreparable havoc if they are not detected promptly and appropriate action taken. As robots are commonly used because power, speed, or resistances to environmental factors need to exceed human capabilities, fault detection is a common and serious concern in the robotics arena. Analytical redundancy (AR) is a fault-detection method that allows us to explicitly derive the maximum possible number of linearly independent control model-based consistency tests for a system. Using a linear model of the system of interest, analytical redundancy exploits the null-space of the state space control observability matrix to allow the creation of a set of test residuals. These tests use sensor data histories and known control inputs to detect any deviation from the static or dynamic behaviors of the model in real time. The standard analytical redundancy fault detection technique is limited mathematically to linear systems. Since analytical redundancy is a model-based technique, it is extremely sensitive to differences between the nominal model behavior and the actual system behavior. A system model with strong nonlinear characteristics, such as a multi-joint robot manipulator, changes significantly in behavior when linearized. Often a linearized model is no longer an accurate description of the system behavior. This makes effective implementation of the analytical redundancy technique difficult, as modeling errors will generate significant false error signals when linear analytical redundancy is applied. To solve this problem we have used nonlinear control theory to extend the analytical redundancy principle into the nonlinear realm. Our nonlinear analytical redundancy (NLAR) technique is applicable to systems described by nonlinear ordinary differential equations and preserves the important formal guarantees of linear analytical redundancy. Nonlinear analytical redundancy generates considerable improvement in performance over linear analytical redundancy when performing fault detection on nonlinear systems, as it removes all of the extraneous residual signal generated by the modeling inaccuracies introduced by linearization, allowing for lower threshold.
Derivation and application of nonlinear analytical redundancy techniques with applications to robotics
(2002) Leuschen, Martin L.; Cavallaro, Joseph R.
Fault detection is important in many robotic applications. Failures of powerful robots, high velocity robots, or robots in hazardous environments are quite capable of causing significant and possibly irreparable havoc if they are not detected promptly and appropriate action taken. As robots are commonly used because power, speed, or resistances to environmental factors need to exceed human capabilities, fault detection is a common and serious concern in the robotics arena. Analytical redundancy (AR) is a fault-detection method that allows us to explicitly derive the maximum possible number of linearly independent control model-based consistency tests for a system. Using a linear model of the system of interest, analytical redundancy exploits the null-space of the state space control observability matrix to allow the creation of a set of test residuals. These tests use sensor data histories and known control inputs to detect any deviation from the static or dynamic behaviors of the model in real time. The standard analytical redundancy fault detection technique is limited mathematically to linear systems. Since analytical redundancy is a model-based technique, it is extremely sensitive to differences between the nominal model behavior and the actual system behavior. A system model with strong nonlinear characteristics, such as a multi-joint robot manipulator, changes significantly in behavior when linearized. Often a linearized model is no longer an accurate description of the system behavior. This makes effective implementation of the analytical redundancy technique difficult, as modeling errors will generate significant false error signals when linear analytical redundancy is applied. To solve this problem we have used nonlinear control theory to extend the analytical redundancy principle into the nonlinear realm. Our nonlinear analytical redundancy (NLAR) technique is applicable to systems described by nonlinear ordinary differential equations and preserves the important formal guarantees of linear analytical redundancy. Nonlinear analytical redundancy generates considerable improvement in performance over linear analytical redundancy when performing fault detection on nonlinear systems, as it removes all of the extraneous residual signal generated by the modeling inaccuracies introduced by linearization, allowing for lower threshold.
Evaluating the reliability of prototype degradable systems
(Elsevier Science Ltd, 2001-04-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
The technique introduced in this paper is a new technique for analyzing fault tolerant designs under considerable uncertainty, such as seen in unique or few-of-a-kind devices in poorly known environments or pre-prototype design analyses. This technique is able to provide useful information while maintaining the uncertainty inherent in the original specifications. The technique introduced here is a logical extension of the underlying concepts of fuzzy sets and Markov models. Although originally developed for robotic systems, the technique is more broadly applicable. This paper develops fuzzy Markov modeling and uses it to analyze a specific robot designed for hazardous waste removal and specific types of electronic systems.
Experimental AR Fault Detection Methods for a Hydraulic Robot
(ISSC, 2000-09-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Gamache, Ronald; Martin, Mike; Center for Multimedia Communication
This paper focuses on practical use and theoretical elaboration of the analytical redundancy technique which is used to efficiently detect faults that have been determined to be mission-hazardous by previous FMECA and fault tree analyses of the Rosie system. We believe we have contributed significant improvements to the potential overall reliability of the system. Additionally, we have expanded the applicability of the AR method to nonlinear systems in the course of our work, making this valuable fault detection method more broadly applicable.
Fault Residual Generation via Nonlinear Analytical Redundancy
(IEEE, 2005-05-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
Fault detection is critical in many applications, and analytical redundancy (AR) has been the key underlying tool for many approaches to fault detection. However, the conventional AR approach is formally limited to linear systems. In this brief, we exploit the structure of nonlinear geometric control theory to derive a new nonlinear analytical redundancy (NLAR) framework. The NLAR technique is applicable to affine systems and is seen to be a natural extension of linear AR. The NLAR structure introduced in this brief is tailored toward practical applications. Via an example of robot fault detection, we show the considerable improvement in performance generated by the approach compared with the traditional linear AR approach.
Investigation of Reliability of Hydraulic Robots for Hazardous Environments Using Analytic Redundancy
(IEEE, 1999-01-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
The Rosie mobile worksystem is a robot that is on the cutting edge of hazardous environment robotics. It is a heavy-duty hydraulic robot designed for nuclear reactor decontamination and dismantlement. The robot consists of a wheeled platform containing a central hydraulic power supply powered by an electric tether, four independently steerable wheels, and a heavy-duty crane/ manipulator. The hydraulic wheel actuator subsystem has been determined to be a vital component of the mobile platform through reliability analysis. Our research into analyzing this robot's reliability through the technique of analytical redundancy (AR) will help provide the Department of Energy (DOE) with a more complete and effective set of tests for monitoring and diagnostics of the Rosie system. In this paper, we discuss the derivation through AR of a suite of model based tests for the default sensor package for one of Rosie's wheel actuators. AR allows us to exploit the sensor information of the sensors values and the system model to derive tests of the consistency of the sensor data. Some of these tests are comparison of the actual system response to control inputs to predicted response indicated by the model, the other tests uncovered by the AR analysis reflect higher order state interdependencies. These tests and their use in monitoring and diagnostics for Rosie are detailed and examined in depth. This work is also an interesting example of the application of model based techniques for an important class of practical systems.
Keeping the Analog Genie in the Bottle: A Case for Digital Robots
(IEEE, 1999-05-01) Walker, Ian D.; Cavallaro, Joseph R.; Leuschen, Martin L.; Center for Multimedia Communication
In this paper, we consider the case for adopting a truly 'digital' type of robot, which would evolve between a discrete and finite set of states. We adopt the point of view that the advantages of traditional robotic evolution (over the full range of a continuous domain) are often negated by complexities associated with the continuous world. The types of discrete robots discussed in this paper would keep this unwanted continuous-time 'genie' in a 'box' of discrete 'steps' during its operation. One distinct advantage of this philosophy is that a formal logical analysis can then be applied to the digital robots, since discrete-time models now correctly and completely model the robot behavior. We argue that there are significant benefits to this strategy in numerous cases, especially with respect to fault detection and fault tolerance. However, there are also disadvantages - in order to guarantee digital behavior, constraints on the robot's operations are imposed. Essentially, we gain formality of digital analysis at the expense of precision of continuous movement. Using an analogy to digital electronics, we discuss ways in which the development of digital robots could revolutionize certain aspects of robotics.
Monitoring and Diagnostics for a Hydraulic Robot in Hazardous Environments
(American Nuclear Society, 1999-04-01) Leuschen, Martin L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia Communication
Hazardous environments are an important application of modern robotic techniques. However, failures can be quite serious in such environments, especially if they trap the robot within or damage containment efforts. Thus robust early fault detection is especially important for such robots. In this paper we discuss the application of one such method, analytical redundancy, to a hydraulic system similar to the one found in hazardous environment robots such as the Rosie worksystem. Further, we extend the method beyond its linear formulation to better deal with the nonlinearities inherent to hydraulic systems.
Nonlinear Fault Detection for Hydraulic Systems
(Springer-Verlag Berlin Heidelberg, 2003-01-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
One of the most important areas in the robotics industry is the development of robots capable of working in hazardous environments. As humans cannot safely or cheaply work in these environments, providing a high level of robotic functionality is important. Our work in this area focuses on a fault detection method known as analytical redundancy, or AR. In this paper we discuss the application to a hydraulic servovalve system of our novel rigorous nonlinear AR technique. AR is a model-based state-space technique that is theoretically guaranteed to derive the maximum number of independent tests of the consistency of sensor data with the system model and past control inputs. Conventional linear AR is only valid for linear sampled data systems. However, our new nonlinear AR (NLAR) technique maintains traditional linear AR’s mathematical guarantee to generate the maximum possible number of independent tests in the nonlinear domain. Thus NLAR allows us to gain the benefits of AR testing for nonlinear systems with both continuous and sampled data.
Nonlinear Fault Detection for Hydraulics: Recent Advances in Fault Diagnosis and Fault Tolerance for Mechatronic Systems
(2002-10-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
One of the most important areas in the robotics industry is the development of robots capable of working in hazardous environments. As humans cannot safely or cheaply work in these environments, providing a high level of robotic functionality is important. Our work in this area focuses on a fault detection method known as analytical redundancy, or AR. In this paper we discuss the application to a hydraulic servovalve system of our novel rigorous nonlinear AR technique. AR is a model-based state-space technique that is theoretically guaranteed to derive the maximum number of independent tests of the consistency of sensor data with the system model and past control inputs. Conventional linear AR is only valid for linear sampled data systems. However, our new nonlinear AR (NLAR) technique maintains traditional linear AR’s mathematical guarantee to generate the maximum possible number of independent tests in the nonlinear domain. Thus NLAR allows us to gain the benefits of AR testing for nonlinear systems with both continuous and sampled data.
Robot Reliability Through Fuzzy Markov Models
(IEEE, 1998-01-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
In the past few years, new applications of robots have increased the importance of robotic reliability and fault tolerance. Standard approaches of reliability engineering rely on the probability model, which is often inappropriate for this task due to a lack of sufficient probabilistic information during the design and prototyping phases. Fuzzy logic offers an alternative to the probability paradigm, possibility, that is much more appropriate to reliability in the robotic context.
Robot Reliability Through Fuzzy Markov Models
(1997-04-20) Leuschen, Martin L.; Center for Multimedia Communications (http://cmc.rice.edu/)
In the past few years, new applications of robots have increased the importance of robotic reliability and fault tolerance. Standard approaches of reliability engineering rely on the probability model, which is often inappropriate for this task due to a lack of sufficient probabilistic information during the design phase. Fuzzy logic offers analternative to the probability paradigm, possibility, that is much more appropriate to reliability in the robotic context. This thesis deals with the construction and interpretation of the fault tree and Markov model reliability tools in a possibilistic (fuzzy) context for robotics. Although fuzzy fault trees are well established reliability tools, fuzzy Markov models have not been used in this context. Additionally, the thesis shows how the possibilistic Markov model used in other contexts is inappropriate in the context of fault tolerance, as it does not preserve the uncertainty information contained in the input. A new reliability method involving the joint use of fault trees and Markov models under fuzziness is developed and applied to examples.
Robot Reliability Using Fuzzy Fault Trees and Markov Models
(SPIE, 1996-11-01) Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia Communication
Robot reliability has become an increasingly important issue in the last few years, in part due to the increased application of robots in hazardous and unstructured environments. However, much of this work leads to complex and nonintuitive analysis, which results in many techniques being impractical due to computational complexity or lack of appropriately complex models for the manipulator. In this paper, we will consider the application of notions and techniques from fuzzy logic, fault trees, and Markov modeling to robot fault tolerance. Fuzzy logic lends itself to quantitative reliability calculations in robotics. The crisp failure rates which are usually used are not actually known, while fuzzy logic, due to its ability to work with the actual approximate (fuzzy) failure rates available during the design process, avoids making too many unwarranted assumptions. Fault trees are a standard reliability tool that can easily assimilate fuzzy logic. Markov modeling allows evaluation of multiple failure modes simultaneously, and is thus an appropriate method of modeling failures in redundant robotic systems. However, no method of applying fuzzy logic to Markov models was known to the authors. This opens up the possibility of new techniques for reliability using Markov modeling and fuzzy logic techniques, which are developed in this paper.
Robotic Fault Detection Using Nonlinear Analytical Redundancy
(IEEE, 2002-05-01) Leuschen, Martin L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia Communication
In this paper we discuss the application of our recently developed nonlinear analytical redundancy (NLAR) fault detection technique to a two-degree of freedom robot manipulator. NLAR extends the traditional linear AR technique to derive the maximum possible number of fault detection tests into the continuous nonlinear domain. The ability to handle nonlinear systems vastly expands the accuracy and viable applications of the AR technique. The effectiveness of the approach is demonstrated through an example.
Testing on the Curve: Nonlinear Analytical Redundancy for Fault Detection
(American Nuclear Society, 2001-03-01) Leuschen, Martin L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia Communication
One of the most important areas in the robotics industry is the development of robots capable of working in hazardous environments. Providing a high level of functionality in these arenas is important simply because humans cannot safely or cheaply work there. Our work focuses on a fault detection method known as analytical redundancy, or AR. AR is a model-based state-space technique that is theoretically guaranteed to derive the maximum number of independent tests of the consistency of sensor data with the system model and past control inputs. AR is only valid for linear sampled data systems. AR is a model-based technique, and is thus extremely sensitive to differences between the nominal model behavior and the actual system behavior. A system with strong nonlinear characteristics, such as a hydraulic servovalve, can be impossible to model properly in the linear domain, creating significant differences between the model and the system that will generate false error signals. In this paper we discuss the application to a hydraulic servovalve system of our novel rigorous nonlinear AR technique that maintains traditional linear AR's theoretical guarantee of the maximum possible number of independent tests in the nonlinear domain. This technique allows us to gain the benefits of AR testing for nonlinear systems with both continuous and sampled data.

Browsing by Author "Leuschen, Martin L."

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