Speed sensorless model predictive control method for a direct-drive wind energy conversion system

In order to obtain the best power in the wind energy conversion system (WECS) of the direct-driven surface-mounted permanent magnet synchronous generator (SPMSG), active disturbance rejection control (ADRC) is introduced to track the motor speed in real time. The control algorithm provides a new design concept and an inherent robust controller component that requires very little system information. Aiming at the problem of system parameter mutation caused by internal factors and external environment changes, an adaptive controller with multi parameter identification is designed, and the disturbance caused by parameter changes is compensated in real time. The model predictive current control (MPC) technology for the sudden change of external environment is designed to accelerate the response speed of the current loop, so as to weaken the estimation of the current disturbance by the active disturbance rejection controller, and make the speed estimation more accurate. Simulation results show that the proposed control strategy is effective and satisfactory.

“…Nowadays, with the development of intelligent control technology, more and more researchers have applied it to wind power generation system. 2…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Composite control for disturbed direct-driven surface-mounted permanent magnet synchronous generator with model prediction strategy

Shi

Nie

2021

“…8 The parameters of an adaptive super-twisting sliding mode controller based on Lyapunov theory are computed via particle swarm optimization (PSO) algorithm for two-axis helicopter in Humaidi and Hasan. 9 An extended state observer–based model predictive technique for the speed control of a permanent magnet synchronous generator is proposed in Li et al, 10 whereas a fuzzy logic controller for the control of droplet movement inside a microfluidic network is formulated in Mehmood et al 11 It is deduced that although a PID controller 12 has a simple structure and easy tuning approaches, however, it is unable to give optimal control in the presence of environmental disturbances, whereas sliding mode control and model predictive control have a complex structure. On the other hand, application of soft computing techniques entails a large simulation time and involves numerous random parameters.…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of fractional order internal model controller design on buck and boost converter

Jain

Hote

Dehuri

et al. 2020

In this paper, fractional order internal model control technique is formulated for non-ideal dc–dc buck and boost converter. The fractional order internal model control approach integrates the concept of Commande Robuste d’Ordre Non Entier principle for tuning a fractional order filter with internal model control scheme. The final controller can be expressed as a series combination of proportional integral derivative controller and a fractional order low pass filter. To assess the robustness of the proposed fractional order internal model control scheme, both the servo response and regulatory response of the dc–dc converters are investigated in the presence of disturbances. The efficacy of fractional order internal model control technique is demonstrated via comparison with 2 degrees of freedom internal model control scheme. Furthermore, an experimental validation of fractional order internal model control is conducted on laboratory setup, and a dSPACE 1104 microcontroller is used for hardware implementation. The simulation results and the hardware validation are a testimony to the effectiveness of fractional order internal model control technique.

“…Specifically, open-loop control strategies include trajectory planning, 29 input shaping, 30,31 and feedforward control. 32 For closed-loop control, there are also many representative approaches such as sliding mode control, [33][34][35] fuzzy control, 36 model predictive control, 37,38 and feedback linearization. 39,40 In different application scenarios, these two kinds of methods have their respective advantages.…”

Section: Introductionmentioning

confidence: 99%

Point-to-point motion control for flexible crane systems working in the deep sea

Wang

Sun

et al. 2020

At present, marine resources, especially the deep-sea resources, are becoming more and more important in resource exploitation globally, and hence, widely used deep-sea cranes are playing essential roles. For such systems, the bridge frames and trolleys are set up above the water, while payloads are transported under the water. In this underwater situation, there exist hydrodynamic forces such as complicated disturbances to the crane systems, making the payload vibration and rope flexibility more obvious. For the sake of improving working efficiency, considering the constraints of all the state variables, an anti-vibration trajectory is designed for the trolley motion, which can not only ensure trolley positioning but also suppress the flexible payload’s vibrations. Then, the state variables are constrained within preset safety ranges. Finally, numerical simulation results prove the satisfactory performance of the designed method.