Comparison of nonlinear dynamic inversion and inverse simulation

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A Comparison of Inverse Simulation-Based Fault Detection in a Simple Robotic Rover with a Traditional Model-Based Method

Robotic rovers which are designed to work in extra-terrestrial environments present a unique challenge in terms of the reliability and availability of systems throughout the mission. Should some fault occur, with the nearest human potentially millions of kilometres away, detection and identification of the fault must be performed solely by the robot and its subsystems. Faults in the system sensors are relatively straightforward to detect, through the residuals produced by comparison of the system output with that of a simple model. However, faults in the input, that is, the actuators of the system, are harder to detect. A step change in the input signal, caused potentially by the loss of an actuator, can propagate through the system, resulting in complex residuals in multiple outputs. These residuals can be difficult to isolate or distinguish from residuals caused by environmental disturbances. While a more complex fault detection method or additional sensors could be used to solve these issues, an alternative is presented here. Using inverse simulation (InvSim), the inputs and outputs of the mathematical model of the rover system are reversed. Thus, for a desired trajectory, the corresponding actuator inputs are obtained. A step fault near the input then manifests itself as a step change in the residual between the system inputs and the input trajectory obtained through inverse simulation. This approach avoids the need for additional hardware on a mass- and power-critical system such as the rover. The InvSim fault detection method is applied to a simple four-wheeled rover in simulation. Additive system faults and an external disturbance force and are applied to the vehicle in turn, such that the dynamic response and sensor output of the rover are impacted. Basic model-based fault detection is then employed to provide output residuals which may be analysed to provide information on the fault/disturbance. InvSim-based fault detection is then employed, similarly providing \textit{input} residuals which provide further information on the fault/disturbance. The input residuals are shown to provide clearer information on the location and magnitude of an input fault than the output residuals. Additionally, they can allow faults to be more clearly discriminated from environmental disturbances

Inverse Simulation as a Tool for Fault Detection and Isolation in Planetary Rovers

With manned expeditions to planetary bodies beyond our own and the Moon currently intractable, the onus falls upon robotic systems to explore and analyse extraterrestrial environments such as Mars. These systems typically take the form of wheeled rovers, designed to navigate the difficult terrain of other worlds. Rovers have been used in this role since Lunokhod 1 landed on the Moon in 1970. While early rovers were remote controlled, communication latency with bodies beyond the Moon and the desire to improve mission effectiveness have resulted in increasing autonomy in planetary rovers. With an increase in autonomy, however, comes an increase in complexity. This can have a negative impact on the reliability of the rover system. With a fault-free system an unlikely prospect and human assistance millions of miles away, the rover must have a robust fault detection, isolation and recovery (FDIR) system. The need for comprehensive FDIR is demonstrated by the recent Chinese lunar rover, Yutu (or “Jade Rabbit”). Yutu was rendered immobile 42 days after landing and remained so for the duration of its operational life: 31 months. While its lifespan far exceeded its expected value, Yutu's inability to move severely impaired its ability to perform its mission. This clearly highlights the need for robust FDIR. A common approach to FDIR is through the generation and analysis of residuals. Output residuals may be obtained by comparing the outputs of the system with predictions of those outputs, obtained from a mathematical model of the system which is supplied with the system inputs. Output residuals allow simple detection and isolation of faults at the output of the system. Faults in earlier stages of the system, however, propagate through the system dynamics and can disperse amongst several of the outputs. This problem is exemplified by faults at the input, which can potentially excite every system state and thus manifest in every output residual. Methods exist for decoupling and analysing output residuals such that input faults may be isolated, however, these methods are complex and require comprehensive development and testing. A conceptually simpler approach is presented in this paper. Inverse simulation (InvSim) is a numerical method by which the inputs of a system are obtained for a desired output. It does so by using a Newton-Raphson algorithm to solve a non-linear model of the system for the input. When supplied with the outputs of a fault-afflicted system, InvSim produces the input required to drive a fault-free system to this output. The fault therefore manifests itself in this generated input signal. The InvSim-generated input may then be compared to the true system input to generate input residuals. Just as a fault at an output manifests itself in the residual for that output alone, a fault at an input similarly manifests itself only in the residual for that input. InvSim may also be used to generate residuals at other locations in the system, by considering distinct subsystems with their own inputs and outputs. This ability is tested comprehensively in this paper. Faults are applied to a simulated rover at a variety of locations within the system structure and residuals generated using both InvSim and conventional forward simulation. Residuals generated using InvSim are shown to facilitate detection and isolation of faults in several locations using simple analyses. By contrast, forward simulation requires the use of complex analytical methods such as structured residuals or adaptive thresholds

Adapting a Remote Laboratory Architecture to Support Collaboration and Supervision

Interest in, and use of, remote laboratories has been rapidly growing. These laboratories provide remote access, via the internet, to real laboratory equipment. Under appropriate circumstances they can support or even replace traditional (proximal) laboratories, provide improved access at reduced cost, and encourage inter-institutional sharing of expensive resources. Most attention to date has been on the development of the core infrastructure that manages access and interaction, and to a lesser extent consideration of pedagogic issues such as which learning outcomes are best suited to this modality. There has however been a recent recognition of the importance of also considering how collaboration and supervision can also be supported. In this paper we discuss a novel approach to the integration of support for multi-user distributed access to a single laboratory instance. The approach retains the benefits of the lightweight client inherent in the underlying architecture

Remote Laboratories in Engineering Education: Trends in Students' Perceptions

Remotely accessible laboratories are an increasingly popular innovation in engineering education. Since 2001, The University of Technology, Sydney has implemented a number of remotely accessible laboratories. This paper presents an analysis of students' feedback responses to their use of the laboratories. The responses show that students not only appreciate the flexibility of the remote access option, but also that they feel that the remote option encourages them to take a deep learning approach to the material

Establishment reality vs maintenance reality: how real is real enough?

Remote and virtual laboratories are increasingly prevalent alternatives to the face-to-face laboratory experience; however, the question of their learning outcomes is yet to be fully investigated. There are many presumptions regarding the effectiveness of these approaches; foremost amongst these assumptions is that the experience must be 'real' to be effective. Embedding reality into a remote or virtual laboratory can be an expensive and time-consuming task. Significant efforts have been expended to create 3D VRML models of laboratory equipment, allowing students to pan, zoom and tilt their perspective as they see fit. Multiple camera angles have been embedded into remote interfaces to provide an increased sense of 'realness'. This paper draws upon the literature in the field to show that the necessary threshold for reality varies depending upon how the students are interacting with the equipment. There is one threshold for when they first interact - the establishment reality - which allows the students to familiarise themselves with the laboratory equipment, and to build their mental model of the experience. There is, however, a second, lower, threshold - the maintenance reality - that is necessary for the students' ongoing operation of the equipment. Students' usage patterns rely upon a limited subset of the available functionality, focusing upon only some aspects of the reality that has been originally established. The two threshold model presented in this paper provides a new insight for the development of virtual laboratories in the future

Simulation Studies Relating to Rudder Roll Stabilization of a Container Ship Using Neural Networks

International audienceRRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

Simulation studies relating to rudder roll stabilization of a container ship using neural networks

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

Low acoustic transmittance through a holey structure

J. S. Bell, I. R. Summers, A. R. J. Murray, Euan Hendry, J. Roy Sambles, and Alastair P. Hibbins, Physical Review B, Vol. 85, article 214305 (2012). Copyright © 2012 by the American Physical Society.The “acoustic double fishnet” is a structure with holes running from its front to back faces, yet at a characteristic frequency it transmits very little sound. The transmittance of this structure, which is comprised of a pair of closely spaced, periodically perforated plates, is determined experimentally and analytically. The surprising acoustic properties are due to hybridization between a two-dimensional resonance within the gap between the plates, and pipe modes within the holes. At the center of the stop band the input impedance is imaginary, interpreted as a negative product of effective bulk modulus and density