150 research outputs found

    Control-Relevant System Identification using Nonlinear Volterra and Volterra-Laguerre Models

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    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

    Tensor Network alternating linear scheme for MIMO Volterra system identification

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    This article introduces two Tensor Network-based iterative algorithms for the identification of high-order discrete-time nonlinear multiple-input multiple-output (MIMO) Volterra systems. The system identification problem is rewritten in terms of a Volterra tensor, which is never explicitly constructed, thus avoiding the curse of dimensionality. It is shown how each iteration of the two identification algorithms involves solving a linear system of low computational complexity. The proposed algorithms are guaranteed to monotonically converge and numerical stability is ensured through the use of orthogonal matrix factorizations. The performance and accuracy of the two identification algorithms are illustrated by numerical experiments, where accurate degree-10 MIMO Volterra models are identified in about 1 second in Matlab on a standard desktop pc

    Inhomogeneous Point-Processes to Instantaneously Assess Affective Haptic Perception through Heartbeat Dynamics Information

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    This study proposes the application of a comprehensive signal processing framework, based on inhomogeneous point-process models of heartbeat dynamics, to instantaneously assess affective haptic perception using electrocardiogram-derived information exclusively. The framework relies on inverse-Gaussian point-processes with Laguerre expansion of the nonlinear Wiener-Volterra kernels, accounting for the long-term information given by the past heartbeat events. Up to cubic-order nonlinearities allow for an instantaneous estimation of the dynamic spectrum and bispectrum of the considered cardiovascular dynamics, as well as for instantaneous measures of complexity, through Lyapunov exponents and entropy. Short-term caress-like stimuli were administered for 4.3?25?seconds on the forearms of 32 healthy volunteers (16 females) through a wearable haptic device, by selectively superimposing two levels of force, 2?N and 6?N, and two levels of velocity, 9.4?mm/s and 65?mm/s. Results demonstrated that our instantaneous linear and nonlinear features were able to finely characterize the affective haptic perception, with a recognition accuracy of 69.79% along the force dimension, and 81.25% along the velocity dimension

    Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

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    Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains

    MODELLING OF NONLINEAR SYSTEMS USING INTEGRATED OBFARX PLUS NEURAL NETWORK MODELS

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    The objective of this project is to develop a new model, which is by combining OBFARX linear model with nonlinear NN model. The results obtained will be compared with the previous models to show performance improvement by the new model. The new model development is based on the model developed by (Zabiri et al 2011) which is OBF linear model combination with nonlinear NN model. The OBF-NN model cannot work efficiently on some problems due to the limitations of the OBF part of the equation. So it is important to analyze the new model which is OBFARX-NN with OBF-NN model. The scope for this project will be the development of the parallel OBFARX-NN model, methods for estimating the model parameter, simulation analysis using MATLAB and evaluation on OBFARX-NN model performance. The method for completing the project will be firstly, make sure all the necessary information about the individual model is available. Then develop a theoretically working OBFARX-NN model. After that, analysis of the performance of the created model is done and also alterations here and there for better clarification. All in all, the result are the improve performance of process control by OBFARX-NN model compared to OBF-NN model.The most important aspect of the model development is the extrapolation capabilities of the model itself. When a model is forced to perform prediction in regions beyond the space of original training, then it can be said that the model can function well even when the process parameter is changed. This aspect is very important because in practical plant, the process conditions are continually changing making extrapolation inevitable. Thus, by testing the extrapolation capabilities of the OBFARX-NN model, the project had come up with the subsequent RMSE value and compared with previous model. The RMSE value indicates superior performance in the extrapolation region

    Identificação e controle de processos via desenvolvimentos em séries ortonormais. Parte B: controle preditivo

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    This paper presents an overview about predictive control schemes based on orthonormal basis function models. Different predictive control schemes based on such models are discussed, namely, linear controllers with terminal (stabilizing) constraints, robust controllers, and non-linear controllers. The discussions comprise a broad bibliographical survey on the subject as well as two case studies involving a simulated dynamic system and a real process.O presente artigo aborda o problema da seleção da estrutura de modelos em algoritmos de controle preditivo para sistemas monovariáveis. Neste sentido, apresenta a utilização de modelos com estrutura dinâmica desenvolvida através de bases de funções ortonormais, como as funções de Laguerre, Kautz ou funções ortonormais generalizadas. Os principais aspectos relacionados com esta classe de modelos no contexto de controladores preditivos lineares com restrições terminais, não lineares e robusto são discutidos e uma revisão bibliográfica é apresentada. O desempenho de malha fechada das estratégias analisadas é ilustrado através de dois casos de estudo envolvendo uma incubadora para recém nascidos e um processo simulado de polimerização isotérmica.322336Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

    Modified Volterra model-based non-linear model predictive control of IC engines with real-time simulations

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    Modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air–fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control
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