On identification of Hammerstein and Wiener model with application to virtualised software system

Aryani, Dharma; Wang, Liuping; Patikirikorala, Tharindu

doi:10.1080/00207721.2016.1244303

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Hammerstein model has already been widely applied in chemical engineering, biology and electrical engineering fields. 4–7 Due to its specific structure to describe nonlinear system, it can bring us easier method to design control system for IPMC. Some researchers also do a few works on Hammerstein modeling of IPMC.…”

Section: Introductionmentioning

confidence: 99%

Hammerstein modeling and hybrid control of force and position for a novel integration of actuating and sensing ionic polymer metal composite gripper

Wang

Gao

Chen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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An innovative integration of actuating and sensing ionic polymer metal composite (IPMC) gripper is proposed and fabricated in this paper. The IPMC gripper is composed of a stationary copper finger and an IPMC finger attached to a force sensor. In order to make IPMC gripper useful in bio-manipulation application, control strategy is a critical factor to resist nonlinear characteristic of IPMC. Hammerstein model of IPMC output displacement is constructed with static nonlinear portion and dynamic linear portion. We utilize creep operator superposition and auto-regression (ARX) models to represent static nonlinear and dynamic linear portions respectively by modeling methods based on data. Then a novel control scheme is proposed and designed using inverse creep compensator for static nonlinear portion and uncertainty state feedback robust control based on state observer for dynamic linear portion. When IPMC reaches a constant displacement to grasp a miniature object, the grasping force may not be provided enough to complete grasping task. Finally, hybrid control of force and position strategy for IPMC gripper is conducted and realized on physical experimental platform. The experimental results demonstrate the effectiveness of control system to guarantee stable manipulation.

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Section: Introductionmentioning

confidence: 99%

Hammerstein modeling and hybrid control of force and position for a novel integration of actuating and sensing ionic polymer metal composite gripper

Wang

Gao

Chen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The importance of H–W model comes from its simplicity and ability to fit various existing systems and describe their dynamic behaviour [1, 2]. For example, it is used to model continuous stirred tank reactor [3], quality of service performance and resource management of software systems [4], pH neutralisation process [5], liquid–liquid heat exchanger system [6], virtualised software system [7], wideband RF transmitters [8] and brushless DC (BLDC) motors [9]. In addition, H–W model is well fitted to systems which boast sensors and actuators with static non‐linear characteristics [2].…”

Section: Introductionmentioning

confidence: 99%

Bayesian approach to identify Hammerstein–Wiener non‐linear model in presence of noise and disturbance

Esmaeilani

Ghaisari

Bagherzadeh

2019

IET Control Theory & Applications

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“…Also, Wiener models and extensions have been used to model biological neurons ( [5]) and muscle fibre activations ( [15]). Software systems can also be studied using a combination of Wiener and other block-oriented models ( [27,2]).…”

Section: Introductionmentioning

confidence: 99%

Bayesian nonparametric identification of Wiener systems

2019

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We propose a nonparametric approach for the identification of Wiener systems. We model the impulse response of the linear block and the static nonlinearity using Gaussian processes. The hyperparameters of the Gaussian processes are estimated using an iterative algorithm based on stochastic approximation expectation maximization. In the iterations, we use elliptical slice sampling to approximate the posterior distribution of the impulse response and update the hyperparameter estimates. The same sampling is finally used to sample the posterior distribution and to compute point estimates. We compare the proposed approach with a parametric approach and a semi-parametric approach. In particular, we show that the proposed method has an advantage when a parametric model for the system is not readily available.

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On identification of Hammerstein and Wiener model with application to virtualised software system

Cited by 9 publications

References 29 publications

Hammerstein modeling and hybrid control of force and position for a novel integration of actuating and sensing ionic polymer metal composite gripper

Hammerstein modeling and hybrid control of force and position for a novel integration of actuating and sensing ionic polymer metal composite gripper

Bayesian approach to identify Hammerstein–Wiener non‐linear model in presence of noise and disturbance

Bayesian nonparametric identification of Wiener systems

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