Intelligent Control Systems and Fuzzy Controllers. II. Trained Fuzzy Controllers, Fuzzy PID Controllers

Vassilyev, S. N.; Kudinov, Y.I.; Olaverri-Monreal, Cristina; Durgaryan, I. S.; Kelina, A.Y.; Kudinov, I. Yu.; Пащенко, А.Ф.

doi:10.1134/s0005117920050112

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…Research is still in progress to mitigate the deficiencies. Control strategy is one such field, with recent publications highlighting the significance of adaptive control (AC) [15], fuzzy control (FC) [16], feedback linearization control (FLC) [17], sliding mode control (SMC), and its improved extension, e.g., merging with proportion integration differentiation (PID) control [18,19], cascade control (CC) [20], etc.…”

Section: Introductionmentioning

confidence: 99%

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

Wang

Yang

et al. 2020

Actuators

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Electro-hydrostatic actuator (EHA) has significance in a variety of industrial tasks. For the purpose of elevating the working performance, we put forward a sliding mode control strategy for EHA operation with a damping variable sliding surface. To start with, a novel sliding mode controller and an extended state observer (ESO) are established to perform the proposed control strategy. Furthermore, based on the modeling of the EHA, simulations are carried out to analyze the working properties of the controller. More importantly, experiments are conducted for performance evaluation based on the simulation results. In comparison to the widely used control strategies, the experimental results establish strong evidence of both overshoot suppression and system rapidity.

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Section: Introductionmentioning

confidence: 99%

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

Wang

Yang

et al. 2020

Actuators

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“…[40][41][42][43][44][45][46] There are many advanced control strategies studied in manipulator control methods, such as model-based control, PID, adaptive, neural network control, SMC, computational torque control, inverse control, fuzzy control, and robust control. [47][48][49][50][51] However, due to the shortcomings of a single control algorithm, a single control algorithm is not used in the process of designing the control strategy. Therefore, multiple control methods are combined so as to resolve the tracking control problem of the manipulator.…”

Section: Related Workmentioning

confidence: 99%

Manipulator trajectory tracking based on adaptive sliding mode control

Zhao

Tao

et al. 2022

Concurrency and Computation

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A manipulator is a complex electromechanical system that is nonlinear, strongly coupled, and uncertain. Achieving its precise and high-quality trajectory control is difficult.Sliding mode control (SMC) is one of the common control methods for manipulators.However, discontinuities in SMC can cause jitter and vibration in the manipulator system, leading to a reduction in the performance of the control system. For the self-adaptive capability jitter vibration problem of SMC, the Dobot magician manipulator is treated as the research object in this article. The dynamics equations of the manipulator are established by Lagrange method, and a simplified model of the manipulator dynamics is constructed. The method of self-adaptive sliding mode control is proposed. Self-adaptive parameters are added to the SMC to achieve self-adaptive adjustment of the SMC parameters. In the MATLAB/Simulink simulation environment analysis show that the self-adaptive SMC method has better self-tuning ability and trajectory tracking ability than the traditional SMC, and weakens the jitter phenomenon existing in the traditional SMC.

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“…D-STATCOM overcomes poor power factor and harmonics. It also acts as a filter, voltage controller at the delivery bus, and load compensator [8][9][10].…”

Section: Voltage Unbalancementioning

confidence: 99%

“…Alternative controllers based on fuzzy logic and neural networks are vigorous and can be developed to function well under a broad range of operating situations [6,7]. More vigorous controllers such as the fuzzy logic method are needed for the DSTATCOM to deliver suitable dynamic voltage regulation and to enhance the power quality and stability of the distribution network [8]. The neural network approach is frequently used in recent times for nonlinear filtering-based networks [9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Power Quality of the Grid Interactive Solar Photovoltaic-Electric Vehicle System

Irfan¹,

Rangarajan

Collins

et al. 2021

WEVJ

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Grid interactive solar photovoltaic (PV) and electric vehicle (EV) systems are the emerging technologies nowadays, mainly due to energy cost reduction and minimization of emission levels. Various research surveys have presented the effect of grid integration of PVs and EVs in an isolated way. However, it is worth accepting that with the continuous emergence of PVs and EVs, the power grid is experiencing the combined effect of PV–EV integration. The distribution system network of EVs impacts the power quality of the grid. In this paper, shunt active power filter is modelled using neuro-fuzzy control technique for the mitigation of harmonics using MATLAB. The improvement in the system performance is analyzed and compared with the traditional compensation techniques.

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Intelligent Control Systems and Fuzzy Controllers. II. Trained Fuzzy Controllers, Fuzzy PID Controllers

Cited by 16 publications

References 20 publications

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

Manipulator trajectory tracking based on adaptive sliding mode control

Enhancing the Power Quality of the Grid Interactive Solar Photovoltaic-Electric Vehicle System

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