Nonlinear Analytical Redundancy Research Articles

Improved Implementation of Nonlinear Analytical Redundancy Relations: Application in Robotics

This paper copes with the problem of detecting actuators’ faults in a two-degree of freedom robot manipulator by using nonlinear analytical redundancy relations composed by derivatives of known signals. To generate the residuals, the redundancy relations are handled with two procedures. The first one allows the determination of a transformation of the base relations to reduce by one the order of the involved derivatives. The second one directly estimates the derivatives of the measurements by using a uniform robust exact differentiator. Simulation results are presented to show the robustness of the nonlinear analytical redundancy relations by using the transformation procedure even in the case of noisy measurements.

Global supervision system for pipelines using nonlinear redundancy relations

The paper deals with a global supervision system for pipelines which involves the diagnostic of leaks and faulty sensors. By assuming a set of nonlinear partial differential equations for the fluid and measurements of flows and pressures at the ends of the pipeline, nonlinear analytical redundancy primary relations are generated which achieve a signatures’ matrix for five faults. Since the redundancy primary relations for the study case depend on time derivatives of known signals, additional manipulations are required for their implementation. Two algorithms are applied to manage this issue: one consists of transforming the primary relations by using proper filters such that a state space realization for the residual is achieved, and the second one consists of a substitution of the derivatives in the relations. The time derivatives are obtained by employing a uniform robust exact differentiation algorithm based on sliding modes. The comparison of the features of the residuals for the five faults with real data of a pilot plant of 200 [m] shows the advantage from the filtering of the redundancy relation with respect to the substitution of an exact time differentiation.

Fault detection and isolation of PEM fuel cell system based on nonlinear analytical redundancy

This paper presents a procedure dealing with the issue of fault detection and isolation (FDI) using nonlinear analytical redundancy (NLAR) technique applied in a proton exchange membrane (PEM) fuel cell system based on its mathematic model. The model is proposed and simplified into a five orders state space representation. The transient phenomena captured in the model include the compressor dynamics, the flow characteristics, mass and energy conservation and manifold fluidic mechanics. Nonlinear analytical residuals are generated based on the elimination of the unknown variables of the system by an extended parity space approach to detect and isolate actuator and sensor faults. Finally, numerical simulation results are given corresponding to a faults signature matrix.

Nonlinear parity space applied to an electric autonomous vehicle

The autonomous navigation of an electric vehicle requires the implementation of a number of sensors and actuators intended to inform it about his environment or his position and velocity and deliver necessary inputs. That's why it is important to detect and locate sensor and actuator faults as soon as possible to enable the operator to run the vehicle in degraded mode or use the fault tolerant control system if it exists. The main purpose of this paper deals with sensors or actuators faults diagnosis of autonomous vehicle. A diagnosis method using a nonlinear model of the vehicle is developed. Nonlinear state space model of the autonomous electric vehicle is used with the method of nonlinear analytical redundancy to detect and to isolate faults occurred on sensors or actuators. Computer simulations are carried out to verify the effectiveness of the method.

Analysis of order of redundancy relation for robust actuator fault detection

This paper presents a new approach, called robust nonlinear analytic redundancy (RNLAR) technique to actuator fault detection for input-affine nonlinear multivariable dynamic systems. Robust fault detection is important because of the universal existence of model uncertainties and process disturbances in most systems. This paper characterizes the order of redundancy relation for nonlinear systems in terms of robustness. A theorem is proposed that state an increase in the order of redundancy relation increases the robustness in the sense of a performance index defined in this paper. Experimental results on a PUMA 560 robotic arm are presented to demonstrate the application of the theorem.

Robust nonlinear analytic redundancy for fault detection and isolation in mobile robot

A robust nonlinear analytical redundancy (RNLAR) technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the efiectiveness of the RNLAR scheme.

Robust Fault Detection of a Robotic Manipulator

In this paper, a new robust fault detection technique for robotic manipulators is developed. The new approach, called robust nonlinear analytic redundancy (RNLAR) technique, detects both sensor and actuator faults in a robotic manipulator The proposed RNLAR technique can compensate for the effects of model-plan-mismatch (MPM) and process disturbance. A nonlinear primary residual vectors (PRV) design method to detect faults is proposed where the PRVs are highly sensitive to faults and less sensitive to MPM and process disturbance. Experimental results on a PUMA 560 are presented to justify the effectiveness of the RNLAR scheme.

ROBUST FAULT DETECTION AND ISOLATION IN MOBILE ROBOT

Robust nonlinear analytical redundancy (RNLAR) technique is used to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are transformed into set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX are presented to justify the effectiveness of the RNLAR scheme.

FAULT DETECTION BASED ON SET-MEMBERSHIP INVERSION

This paper deals with a set-membership method for fault detection. Based on interval analysis, the proposed approach focuses on the design of consistency tests for dynamical systems with additive and multiplicative parameter uncertainties. Instead of canceling uncertainties following the example of the so-called robust approaches, uncertain analytical redundancy relations take uncertainties into account as bounded variables. The use of a set-membership inversion method allows non-linear analytical redundancy relations to be directly processed without requiring any linearization.

Fault residual generation via nonlinear analytical redundancy

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.