A high order sliding mode observer for systems in triangular input observer form

Sánchez‐Torres, Juan Diego; Loukianov, Alexander G.; Murillo, Julián Alberto Patiño; Giraldo, Bertulfo; Botero, Héctor

doi:10.1109/larc.2011.6086833

Cited by 3 publications

(8 citation statements)

References 19 publications

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“…Please note that the structure (36) for designing a control law like (39) is included in the design of the RHONN reduced-order observer mathematical model (41) for an LIM, which considers two blocks  1 (k) (42) and  2 (k) (43) where vector X d 1 is given by the desired references to be tracked, and vector X d 2 is calculated as expressed in Equation (35). In this case, control of the flux magnitude of the LIM is required, which its estimation is defined aŝ (44).…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, initial values for weight vectors are selected randomly, except for the weights related to pseudo-control and control inputs, correspondingly, which are fixed for controllability purposes. 41 For training, initial values of EKF are selected heuristically in order to improve neural network performance.…”

Section: Resultsmentioning

confidence: 99%

“…It is important to remark that for the implementation of the super-twisting reduced-order observer is necessary knowledge about the system model, 43 which is also true for other sliding modes based observers like the ones presented on the following references. 41,44 Results of the described scheme are shown in Figures 7 and 8. Table 3 shows the estimation root mean square errors for the measurable state variables x 1 , x 2 , x 5 , and x 6 , of both simulations.…”

Section: Test Descriptionmentioning

confidence: 99%

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Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Rios

Alanis

Arana‐Daniel

et al. 2020

Intl J Robust & Nonlinear

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This work presents a neural observer-based controller for uncertain nonlinear discrete-time systems with unknown time-delays. The proposed neural observer does not need previous knowledge of the model about the system under consideration, neither the value of its parameters, delays, nor their explicit estimations. The proposed neural observer is based on a neural network composed of two recurrent high order neural networks (RHONNs) for nonmeasurable state variables, one in a parallel configuration, and for measurable state variables one in a series-parallel configuration. The neural network is trained on-line with an extended Kalman filter algorithm. The proposed RHONN observer provides a mathematical model for the system. Based on such a resulting mathematical model, a control law is designed using discrete-time sliding mode block control. Applicability is presented using real-time results that show the performance of the proposal using a linear induction motor prototype as the selected system; this prototype is under the presence of varying time-delays. A Lyapunov analysis is included to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed RHONN observer-controller scheme for uncertain nonlinear discrete-time systems with unknown delays. K E Y W O R D S discrete-time sliding modes, Lyapunov stability, neural block control, neural state estimation, real-time, time-delay systems 8402

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Test Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Rios

Alanis

Arana‐Daniel

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…By virtue of the aforementioned sliding mode differentiators, several state observers (SO) have also been reconstructed to estimate the unmeasurable state variables of various kinds of systems such as , which can also be utilized to deal with the challenging control problems of output feedback stabilization and tracking. Davila et al .…”

Section: Introductionmentioning

confidence: 99%

“…have presented globally finite time observers for non‐linear systems by replacing the constant gains of the high gain observer with arbitrary exponentially growing gains. The well‐known HOSD technique has also been utilized for observing the unmatched uncertainty for linear systems in and states for the triangular form of non‐linear systems in . However, the HOSD technique requires the bounds on the uncertainties to compute the observer gains, which is practically impossible.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of a New Robust Exponential Sliding Mode Differentiator with its Observer Applications

Deepika

Narayan

Kaur

2018

Asian Journal of Control

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This paper proposes a new robust exponential sliding mode differentiator for estimating the (n À 1) derivatives of a non-linear function. Finite time convergence, robustness, and exactness are also ensured analytically with the proposed methodology. In addition, the results of the proposed sliding mode differentiator are extended to derive theorems for the novel state (SO) and extended state observers (ESO), which would estimate the system states as well as uncertainties recursively in finite time. Finally, three examples are implemented to validate the proposed methodologies and the obtained simulations are compared with the previously developed methods in literature.

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Comparación de desempeño de observadores de estado en sistemas lineales con aplicación a un motor de corriente continua

Florián

2018

Rev. Cintex

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En muchos procesos industriales existen variables importantes desde el punto de vista de control de procesos que requieren de vigilancia y monitoreo constante; sin embargo, muchas veces es una tarea de gran dificultad o simplemente no existe un sensor adecuado para realizar esta medición. Esto genera un gran reto para los ingenieros quienes deben, de alguna manera, tener medición sobre las variables del sistema. Una alternativa cuando no se tiene acceso completo al vector de estados del sistema, es obtener una estimación de los estados no medibles a través de un observador de estados, sistema dinámico cuyos estados convergen a los del sistema observado, basados en el modelo del sistema y las medidas disponibles de la entrada y la salida de este. Estos observadores sirven para estimar variables que sean difíciles de medir, de tal manera que se pueda describir el comportamiento del sistema dinámico. En este trabajo se prueban una serie de observadores de estado para estimar la velocidad de un motor de corriente continua (DC). Utilizando diferentes estimadores de estados tales como el Estimador Luenberger, el Filtro de Kalman para sistemas lineales y el Observador por Modos Deslizantes se pretende estimar correctamente la velocidad del motor DC partiendo de su modelo matemático y la medición de corriente del estator. Tanto en la implementación como en la simulación de los observadores, se observa que las perturbaciones inesperadas podrían alterar el comportamiento del sistema. Los resultados apuntan a que cada observador tiene unas condiciones específicas en las que su desempeño es más efectivo.

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A high order sliding mode observer for systems in triangular input observer form

Cited by 3 publications

References 19 publications

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Synthesis of a New Robust Exponential Sliding Mode Differentiator with its Observer Applications

Comparación de desempeño de observadores de estado en sistemas lineales con aplicación a un motor de corriente continua

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