Grey wolf optimizer for identification of liquid slosh behavior using continuous-time hammerstein model

Abstract

This paper presents the identification of liquid slosh plant using the Hammerstein model based on Grey Wolf Optimizer (GWO) method. A remote car that carrying a container of liquid is considered as the liquid slosh experimental rig. In contrast to other research works, this paper consider a piece-wise affine function in the nonlinear function of the Hammerstein model, which is more generalized function. Moreover, a continuous-time transfer function is utilized in the Hammerstein model, which is more suitable to represent a real system. The GWO method is used to tune both coefficients in the nonlinear function and transfer function of the Hammerstein model such that the error between the identified output and the real experimental output is minimized. The effectiveness of the proposed framework is assessed in terms of the convergence curve response, output response, and the stability of the identified model through the bode plot and pole zero map. The results show that the GWO based method is able to produce a Hammerstein model that yields identified output response close to the real experimental slosh output.

Experimental study of the VFD’s speed stabilization efficiency under torque disturbances

“…From these diagrams Figure 8 it follows that VFDs with "traditional" sensorless controls (vector and scalar) have practically the same stabilization characteristics under torque disturbances. An electric drive with dynamic positive feedback on the stator current (DPF), in which, as shown by numerous studies [19], a constant magnetic flux of the rotor is formed, have the best characteristics under periodic torque disturbances.…”

A multipart distributed modelling approach for an automated protection system in open-air cloth drying

“…A trilateration estimation method described in the study [21] is adopted to ensure that each anchor node stores its position coordinates relative to data reception from the targeted node, and use the estimated distance in-between estimate the adjustable humiture parameter for condition monitoring. The state equation of the sensing unit becomes (7), and the output equation becomes (8). With the application of Taylor's series approximation, the sensing node model presents a form as stated in ( 9), (10).…”

“…The cloth washing real systems can be modelled against such Hammerstein model with continuous-time transfer function, based on using the Grey Wolf Optimizer method to tune both the coefficients of the nonlinear and transfer functions of the model. The identification of the liquid sloth behavior framework can be incorporated into minimizing the error between the identified output and the main experimental output [8]. The control mechanism of the drying and protection system can be considered using the previous works relative to parametric estimation of modelled multiple-input-multiple-output systems and mobile robots [9], [10].…”

Optimal tuning of a wind plant energy production based on improved grey wolf optimizer

“…Alternatively, a grey wolf optimizer (GWO) [11], which is swarm-based inspired by social behavior of groups of animals (grey wolves), has been successfully solved numerous types of real applications. For instance, improving wind plant production [12], solving optimal reactive power dispatch problem [13], automatic generation control of interconnected power system [14], design for a photovoltaic (PV) [15,16], vehicle engine [17], unmanned aerial vehicle (UAV) [18], facial image [19], image segmentation [20], gridconnected permanent-magnet synchronous generator [21], satellite image segmentation [22], hybrid renewable energy system PV-diesel generator-battery [23], and liquid slosh system identification [24]. GWO algorithm is inspired by the social hierarchy of grey wolves that divided into four groups, which are alphas, beta, delta and omega.…”