Navegando por Assunto "Systems identification"

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Acesso aberto (Open Access)
Análise de sistemas não-lineares e síntese de operadores inversos por séries de volterra diagonais
(Universidade Federal do Pará, 2019-08-22) TEIXEIRA, Raphael Barros; BAYMA, Rafael Suzuki; http://lattes.cnpq.br/6240525080111166; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
This work proposes innovative strategies for the analysis of nonlinear systems and the synthesis of inverse operators using the Volterra diagonal series. By expressing the output explicitly from the input, the Volterra series enable nonlinear analysis in the frequency domain. However, the multidimensional nature of the model confers several difficulties to its systematic use. This work takes a new look at the so-called Volterra series in diagonal coordinates, in which Volterra operators are expressed as a set of linear and one-dimensional filters that process nonlinear polynomial terms of the input. The proposition of the rational form for these filters leads to exact and compact Volterra models, which exhibit a direct connection with modern nonlinear formalisms, notably the Wiener and Hammerstein block structured models, and the non-linear, autoregressive polynomial models with exogenous input (NARX). In particular, it is proposed a strategy to obtain diagonal Volterra models from the polynomial NARX. The strategy is called derivative method, because it depends only on the established results of the differential calculus. This is important because a NARX model can fit relatively well to experimental data to describe a wide variety of practical systems. A subsequent study through the Volterra series comes as an additional natural step of analysis. This result also opens up possibilities for non-linear synthesis. A problem that has received increasing attention in systems engineering is that of the synthesis of inverse nonlinear operators, through which it tries to reverse distortions generated by the underlying system, preserving the integrity of the information of interest. In this case we propose a strategy of synthesis of Volterra inverse diagonal operators for particular classes of nonlinear polynomial models. It is a numerical approach where the synthesis is driven by an optimization problem that is inspired by the classic inverse p-order operator. Keywords: Non-linear systems, Volterra series diagonals, systems identification, nonlinear analysis, dynamic inversion
Acesso aberto (Open Access)
Metodologia para estimação de intenção de movimento e controle em tempo real de prótese mioelétrica de mão: uma abordagem linear, preditiva e estocástica
(Universidade Federal do Pará, 2018-03-28) DUTRA, Bruno Gomes; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
Muscle signals from electromyography (EMG) are widely used to detect muscle contraction and intention to motion. By using these signals in real time in prosthetic control, a low signal to noise ratio is commonly found. Thus, it is necessary to have recursive methods, robust to noise and efficient algorithms, to generate commands in real time for the robotic actuator. In this research, stochastic system indentification techniques, Kalman filter, sensor fusion and stochastic predictive control techniques were investigated and applied to improve the measurement and processing of electromyographic signals to increase robustness in the control of biomechatronic prostheses. Thus, it is an improved process, less sensitive to noise and with minimal delays and phase lags. In this methodology, a four-stage distribution method is used: (1) features extraction by using an autoregressive model (AR), (2) data fusion with the Kalman filter, (3) motion estimation algorithm, and (4) predictive control with the generalized minimum variance controller applied to a servomechanism. The main objectives were: to enhance the signal-to-noise ratio of EMG signals, to have a low-cost real-time processing man-machine interface, to avoid measurement problems and to minimize energy consumption of the control system. A didactic plant was developed, which is a 4 channel EMG data acquisition and processing system with a servomechanism and its control system coupled in a robotic jaw. Practical tests were conducted with the prototype and the results show that it is possible to continuously estimate the intention of opening and closing movement of the hand and can confirm the good performance of the stochastic controller designed for the control of the electric prosthesis.