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D. J. Brookfield

Bio: D. J. Brookfield is an academic researcher from University of Liverpool. The author has contributed to research in topics: Friction torque & Rolling resistance. The author has an hindex of 2, co-authored 2 publications receiving 33 citations.

Papers
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Journal ArticleDOI
TL;DR: The practical application of the nonlinear filtering approach of Detchmendy and Sridhar, on both computer simulation and experimental data, in frictional identification confirms the feasibility of the proposed estimation approach and justifies the introduction of asymmetry.

32 citations

Journal ArticleDOI
01 Nov 1996
TL;DR: In this paper, an experimental comparison of five methods of identifying friction in robot drives is presented, and it is shown that an asymmetric Coulomb and viscous model properly identifies the frictional torque due to the combined sliding and rolling friction in the motor.
Abstract: This paper presents an experimental comparison of five methods of identifying friction in robot drives. The methods considered are direct plotting of velocity versus armature voltage, plotting velocity versus armature current, third harmonic estimation, batch least squares and sequential least squares. These methods were implemented on a d.c. servo motor robot drive system to identify Coulomb and viscous friction parameters. It is shown that an asymmetric Coulomb and viscous model properly identifies the frictional torque due to the combined sliding and rolling friction in the motor. Furthermore, although each of the identification methods is shown to be capable of giving reasonable estimates of the frictional coefficients, the plotting of velocity versus armature current is shown to be most suitable for off-line frictional identification and the sequential least-squares method most suitable for on-line identification, particularly when coefficients may change with time.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: A tracking controller is developed in this paper for a general Euler-Lagrange system that contains a new continuously differentiable friction model with uncertain nonlinear parameterizable terms, and a recently developed integral feedback compensation strategy is used to identify the friction effects online.
Abstract: Modeling and compensation for friction effects has been a topic of considerable mainstream interest in motion control research. This interest is spawned from the fact that modeling nonlinear friction effects is a theoretically challenging problem, and compensating for the effects of friction in a controller has practical ramifications. If the friction effects in the system can be accurately modeled, there is an improved potential to design controllers that can cancel the effects; whereas, excessive steady-state tracking errors, oscillations, and limit cycles can result from controllers that do not accurately compensate for friction. A tracking controller is developed in this paper for a general Euler-Lagrange system that contains a new continuously differentiable friction model with uncertain nonlinear parameterizable terms. To achieve the semi-global asymptotic tracking result, a recently developed integral feedback compensation strategy is used to identify the friction effects online, assuming exact model knowledge of the remaining dynamics. A Lyapunov-based stability analysis is provided to conclude the tracking and friction identification results. Experimental results illustrate the tracking and friction identification performance of the developed controller.

256 citations

Journal ArticleDOI
TL;DR: The modified identification algorithm does not suffer from the problem of nonlinear distortions in the signal shape and is able to determine the nonlinear friction such that an accurate servo system model can be derived.
Abstract: Mechanical devices usually come with undesirable nonlinearities, such as friction, backlashes, and saturations. Under the assumption of linear systems, the commonly seen identification schemes utilize sinusoidal excitation signals for parameter identification. However, the data needed for identification are unavoidably distorted by the aforementioned nonlinearities and the identification result may not be satisfactory. In the paper, binary test signals are used to perform identification, thus simplifying the behavior of friction. An identification method based on the difference of binary multifrequency excitation signals is proposed. The modified identification algorithm does not suffer from the problem of nonlinear distortions in the signal shape and is able to determine the nonlinear friction such that an accurate servo system model can be derived. A high-precision ball-screw table with asymmetric friction is identified as a test plant for this approach. The results prove that the method can be used very successfully.

68 citations

Journal ArticleDOI
TL;DR: This paper provides a detailed analysis of the practical stabilizability of systems in terms of the size of hypercubes bounding the initial conditions, the state transient, and the steady-state evolution and an explicit construction of a practically stabilizing controller for the quantized I/O case.
Abstract: This paper is concerned with the stabilization of discrete-time linear systems with quantization of the input and output spaces, i.e., when available values of inputs and outputs are discrete. Unlike most of the existing literature, we assume that how the input and output spaces are quantized is a datum of the problem, rather than a degree of freedom in design. Our focus is hence on the existence and synthesis of symbolic feedback controllers, mapping output words into the input alphabet, to steer a quantized I/O system to within small invariant neighborhoods of the equilibrium starting from large attraction basins. We provide a detailed analysis of the practical stabilizability of systems in terms of the size of hypercubes bounding the initial conditions, the state transient, and the steady-state evolution. We also provide an explicit construction of a practically stabilizing controller for the quantized I/O case.

63 citations

Proceedings ArticleDOI
08 Jun 2005
TL;DR: In this article, a tracking controller is developed for a general Euler-Lagrange system that contains a new continuously differentiable friction model with uncertain nonlinear parameterizable terms, and a recently developed integral feedback compensation strategy is used to identify the friction effects on-line.
Abstract: Modeling and compensation for friction effects has been a topic of considerable mainstream interest in motion control research. This interest is spawned from the fact that modeling nonlinear friction effects is a theoretically challenging problem, and compensating for the effects of friction in a controller has practical ramifications. If the friction effects in the system can be accurately modeled, there is an improved potential to design controllers that can cancel the effects; whereas, excessive steady-state tracking errors, oscillations, and limit cycles can result from controllers that do not accurately compensate for friction. A tracking controller is developed in this paper for a general Euler-Lagrange system that contains a new continuously differentiable friction model with uncertain nonlinear parameterizable terms. To achieve the semi-global asymptotic tracking result, a recently developed integral feedback compensation strategy is used to identify the friction effects on-line, assuming exact model knowledge of the remaining dynamics. A Lyapunov-based stability analysis is provided to conclude the tracking and friction identification results. On-going efforts are being directed at the development of an experimental testbed to illustrate the tracking and friction identification performance of the developed controller.

37 citations

Journal ArticleDOI
TL;DR: A generic framework for the enhancement of advanced physics-based models with degradation curves is introduced by introducing a generic framework in a case study coming from the white goods industry, where it is investigated whether the robot will experience some failure within the next 18 months.
Abstract: Predictive maintenance has been proposed to maximize the overall plant availability of modern manufacturing systems. To this end, research has been conducted mainly on data-driven prognostic techniques for machinery equipment individual components. However, the lack of historical data together with the intricate design of industrial machines, e.g. robots, stimulate the use of advanced methods exploiting simulation capabilities. This paper aims to address this challenge by introducing a generic framework for the enhancement of advanced physics-based models with degradation curves. The creation of a robot's simulation model and its enrichment with data from the degradation curves of the robot's components is presented. Following, the extraction of information from degradation curves during the simulation of the robot's dynamic behaviour is addressed. The Digital Twin concept is employed to monitor the health status of the robot and ensure the convergence of the simulated to the actual robot behaviour. The output of the simulation can enable to estimate the future behaviour of the robot and make predictions for the quality of the products to be produced, as well as to estimate the robot's Remaining Useful Life. The proposed approach is applied in a case study coming from the white goods industry, where it is investigated whether the robot will experience some failure within the next 18 months.

35 citations