Frequency identification of nonparametric Wiener systems containing backlash nonlinearities

Giri, F.; Rochdi, Y.; Radouane, Abdelhadi; Brouri, Adil; Chaoui, F.Z.

doi:10.1016/j.automatica.2012.08.043

Cited by 47 publications

(36 citation statements)

References 12 publications

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“…In analogy with the Wiener model, the cascade model structure consisting of a linear dynamic block followed by a nonlinear dynamic block was often used for the identification of nonlinear dynamic systems with output dynamic nonlinearities, e.g., in [24][25][26]. However, in some cases of more complex nonlinear dynamic systems with actuator nonlinearities such as saturations, the twoblock cascade model may be not precise enough and it is appropriate to choose a three-block cascade model with combination of nonlinear static, linear dynamic, and nonlinear dynamic blocks.…”

Section: Introductionmentioning

confidence: 99%

Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models

Vörös

2014

Journal of the Franklin Institute

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Section: Introductionmentioning

confidence: 99%

Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models

Vörös

2014

Journal of the Franklin Institute

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“…It is not a simple task to define the parameters of the linear dynamic model approximation in the closed-loop system. In [47][48][49] active methods of identification are proposed; here sine waves in input are used to excite the Wiener continuous-time system and frequency methods are used to determine the unknowns. Unknown additive disturbances create problems for closedloop identification [50].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Manenti

Rossi

Goryunov

et al. 2015

Resource-Efficient Technologies

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Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior.In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

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“…One is the parametric method, which regards the entire system or subsystem as a known structure [10,11]. The other is the nonparametric method in which none of the system's linear or nonlinear parts is assumed to be a parameterized model [12][13][14]. In this study, the nonparametric method is adopted.…”

Section: Introductionmentioning

confidence: 99%

Novel Wiener models with a time-delayed nonlinear block and their identification

2016

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Popular Wiener models are a combination of dominant linear behavior and static nonlinear characteristics. However, they are less accurate in dealing with problems with strong nonlinear time-delayed effects. Hence, a group of novel nonlinear models based on Wiener models are proposed in this study. The static nonlinear part of general Wiener models is replaced by a time-delayed nonlinear block. Thus, two novel Wiener models with a quasi-dynamic nonlinear block are constructed, called the Wiener-QDN models. The autoregressive with exogenous input (ARX) model is utilized to identify the dynamic linear block, and the radial basis function neural network is used to build the nonlinear part of Wiener and the Wiener-QDN models. Results reveal the effectiveness of these models in nonlinear system identification. The first test cases are systems governed by simple equations. The Wiener-QDN models can identify these systems with or without measurement noise. These models are further applied in unsteady aerodynamic modeling cases. Test cases of a NACA 0012 airfoil pitching in transonic flow indicate that both linearity and nonlinearity are well captured by the proposed Wiener-QDN models.

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Frequency identification of nonparametric Wiener systems containing backlash nonlinearities

Cited by 47 publications

References 12 publications

Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models

Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Novel Wiener models with a time-delayed nonlinear block and their identification

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