2,098 research outputs found
Feedrate planning for machining with industrial six-axis robots
The authors want to thank Stäubli for providing the necessary information of the controller, Dynalog for its contribution to the experimental validations and X. Helle for its material contributions.Nowadays, the adaptation of industrial robots to carry out high-speed machining operations is strongly required by the manufacturing industry. This new technology machining process demands the improvement of the overall performances of robots to achieve an accuracy level close to that realized by machine-tools. This paper presents a method of trajectory planning adapted for continuous machining by robot. The methodology used is based on a parametric interpolation of the geometry in the operational space. FIR filters properties are exploited to generate the tool feedrate with limited jerk. This planning method is validated experimentally on an industrial robot
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from
limit order book (LOB) data of cash equities. The architecture utilises
convolutional filters to capture the spatial structure of the limit order books
as well as LSTM modules to capture longer time dependencies. The proposed
network outperforms all existing state-of-the-art algorithms on the benchmark
LOB dataset [1]. In a more realistic setting, we test our model by using one
year market quotes from the London Stock Exchange and the model delivers a
remarkably stable out-of-sample prediction accuracy for a variety of
instruments. Importantly, our model translates well to instruments which were
not part of the training set, indicating the model's ability to extract
universal features. In order to better understand these features and to go
beyond a "black box" model, we perform a sensitivity analysis to understand the
rationale behind the model predictions and reveal the components of LOBs that
are most relevant. The ability to extract robust features which translate well
to other instruments is an important property of our model which has many other
applications.Comment: 12 pages, 9 figure
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Fast bias-constrained optimal FIR filtering for time-invariant state space models
This paper combines the finite impulse response filtering with the Kalman structure (predictor/corrector) and proposes a fast iterative bias-constrained optimal finite impulse response filtering algorithm for linear discrete time-invariant models. In order to provide filtering without any requirement of the initial state, the property of unbiasedness is employed. We first derive the optimal finite impulse response filter constrained by unbiasedness in the batch form and then find its fast iterative form for finite-horizon and full-horizon computations. The corresponding mean square error is also given in the batch and iterative forms. Extensive simulations are provided to investigate the trade-off with the Kalman filter. We show that the proposed algorithm has much higher immunity against errors in the noise covariances and better robustness against temporary model uncertainties. The full-horizon filter operates almost as fast as the Kalman filter, and its estimate converges with time to the Kalman estimate
Control-Relevant System Identification using Nonlinear Volterra and Volterra-Laguerre Models
One of the key impediments to the wide-spread use of nonlinear control in industry is the availability of suitable nonlinear models. Empirical models, which are obtained from only the process input-output data, present a convenient alternative to the more involved fundamental models. An important advantage of the empirical models is that their structure can be chosen so as to facilitate the controller design problem. Many of the widely used empirical model structures are linear, and in some cases this basic model formulation may not be able to adequately capture the nonlinear process dynamics. One of the commonly used nonlinear dynamic empirical model structures is the Volterra model, and this work develops a systematic approach to the identification of third-order Volterra and Volterra-Laguerre models from process input-output data.First, plant-friendly input sequences are designed that exploit the Volterra model structure and use the prediction error variance (PEV) expression as a metric of model fidelity. Second, explicit estimator equations are derived for the linear, nonlinear diagonal, and higher-order sub-diagonal kernels using the tailored input sequences. Improvements in the sequence design are also presented which lead to a significant reduction in the amount of data required for identification. Finally, the third-order off-diagonal kernels are estimated using a cross-correlation approach. As an application of this technique, an isothermal polymerization reactor case study is considered.In order to overcome the noise sensitivity and highly parameterized nature of Volterra models, they are projected onto an orthonormal Laguerre basis. Two important variables that need to be selected for the projection are the Laguerre pole and the number of Laguerre filters. The Akaike Information Criterion (AIC) is used as a criterion to determine projected model quality. AIC includes contributions from both model size and model quality, with the latter characterized by the sum-squared error between the Volterra and the Volterra-Laguerre model outputs. Reduced Volterra-Laguerre models were also identified, and the control-relevance of identified Volterra-Laguerre models was evaluated in closed-loop using the model predictive control framework. Thus, this work presents a complete treatment of the problem of identifying nonlinear control-relevant Volterra and Volterra-Laguerre models from input-output data
Data-driven vibration prognosis using multiple-input finite impulse response filters and application to railway-induced vibration of timber buildings
With this paper, we present a vibration prognosis method based on finite impulse
responses. The impulse responses are identified using measurement data from an
existing building and consider a multiple-input/multiple-output topology.
Vibration prognosis in urban buildings is becoming increasingly important, since more
and more buildings are being constructed close to urban infrastructure. Combined with
modern and ecological choices of building materials and the low vibration levels
required by current standards, serviceability in terms of structural dynamics becomes
an issue. Sources of vibration in urban settings include railway and metro lines as well
as road traffic. This work focuses on a method especially suited to the three-
dimensional vibration state encountered in modern timber buildings. Under the
assumption of linear time-invariant structural dynamic behaviour, we develop a time-
domain identification approach. The novelties of this contribution lie in the formulation
of a numerically efficient method to identify multiple-input finite impulse response filters
and its application to measurement data of a timber building.
We validate this data-driven prognosis method using measurement data from a
building constructed from cross-laminated timber, considering the three-dimensional
vibration behaviour. The accuracy and limitations are assessed using railway-induced
vibrations as a typical source of disturbance by infrastructure. We show that vibration
data from the foundation can be used for effective prognosis of the top floor slabs
considering train types not included in the identification data set. Based on the
prognosis method, a virtual sensor concept for long-term monitoring is presented
System Level Synthesis
This article surveys the System Level Synthesis framework, which presents a
novel perspective on constrained robust and optimal controller synthesis for
linear systems. We show how SLS shifts the controller synthesis task from the
design of a controller to the design of the entire closed loop system, and
highlight the benefits of this approach in terms of scalability and
transparency. We emphasize two particular applications of SLS, namely
large-scale distributed optimal control and robust control. In the case of
distributed control, we show how SLS allows for localized controllers to be
computed, extending robust and optimal control methods to large-scale systems
under practical and realistic assumptions. In the case of robust control, we
show how SLS allows for novel design methodologies that, for the first time,
quantify the degradation in performance of a robust controller due to model
uncertainty -- such transparency is key in allowing robust control methods to
interact, in a principled way, with modern techniques from machine learning and
statistical inference. Throughout, we emphasize practical and efficient
computational solutions, and demonstrate our methods on easy to understand case
studies.Comment: To appear in Annual Reviews in Contro
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