Direct inverse control of cable-driven parallel system based on type-2 fuzzy systems

Wang, Tiechao; Tong, Shaocheng

doi:10.1016/j.ins.2015.03.014

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…A parallel cable-driven lifting mechanism was also controlled using T2 FSs [73]. This problem was split into six subsystems, where each subsystem had an IT2 fuzzy nonlinear autoregressive exogenous (NARX) model.…”

Section: It2mentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

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Section: It2mentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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“…However, the cable-driven structures also introduce significant challenges to the dynamic control of CDPMs, such as elastic deformation, sagging, and unidirectional force. Researchers have developed several methods to solve these problems, including sliding mode control (SMC), adaptive control, adaptive sliding mode control, fuzzy logic control, and neural networks [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Backstepping Motion Control of Underwater Cable-Driven Parallel Mechanism Using Improved Linear Model Predictive Control

Zhao

Qin

et al. 2023

JMSE

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This paper proposes a novel motion-tracking control methodology for an underwater cable-driven parallel mechanism (CDPM) that achieves calculation of dynamic tension constraint values, tension planning, parameter linearization, and motion tracking. The control objective is divided into three sub-objectives: motion tracking, horizontal displacement suppression, and cable-tension restriction. A linear model predictive control (LMPC) method is designed to plan cable tensions for motion-tracking and displacement suppression. The robust adaptive backstepping controller converts cable tension into winch speed based on the joint-space method and command filtering. Moreover, the X−swapping method is used to linearize and identify the time−varying nonlinear parameters. An essential prerequisite for restricting cable tension is to obtain cable-tension constraint values. A novel dynamic minimum tension control (DMTC) method, based on the equivalent control concept, is proposed for this aim. The DMTC can adaptively obtain the lower cable-tension threshold through the platform posture and motion status, anchor distribution position, and cable integrity status. Compared to traditional fixed tension constraint values, DMTC can more effectively cope with sudden changes in cable tension than fixed tension constraints. Finally, several simulations are carried out to verify the effectiveness and robustness of the proposed approach.

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“…Among them, the adaptive inverse fuzzy controller based on the fuzzy model was widely used. Commonly included are the T-S fuzzy model based on the fuzzy tree model [17], T-S fuzzy model based on the grid method [18], T-S fuzzy model based on entropy clustering and fuzzy partition [19], recursive least squares support vector machine [20], type 2 fuzzy model [21][22][23], and so on. In practice, adaptive inverse control has also been successfully applied, including in pneumatic loading systems [24], cable-driven parallel systems [25,26], pH neutralization processes [27], and chatter vibrations in internal turning operations [28].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

Zhong

2019

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Establishing an accurate inverse model is a key problem in the design of adaptive inverse controllers. Most real objects have nonlinear characteristics, so mathematical expression of an inverse model cannot be obtained in most situation. A Takagi–Sugeno(T-S)fuzzy model can approximate real objects with high precision, and is often applied in the modeling of nonlinear systems. Since the consequent parameters of T-S fuzzy models are linear expressions, this paper firstly uses a fuzzy c-regression model (FCRM) clustering algorithm to establish inverse fuzzy model. As the least mean square (LMS) algorithm is only used to adjust consequent parameters of the T-S fuzzy model in the process of parameter adjustment, the premise parameters are fixed and unchanged in the process of adjustment. In this paper, the back propagation (BP) algorithm is applied to adjust the premise and consequent parameters of the T-S fuzzy model, simultaneously online. The simulation results show that the error between the system output controlled by proposed adaptive inverse controller and the desired output is smaller, also the system stability can be maintained when the system output has disturbances.

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Direct inverse control of cable-driven parallel system based on type-2 fuzzy systems

Cited by 8 publications

References 30 publications

Review of Recent Type-2 Fuzzy Controller Applications

Review of Recent Type-2 Fuzzy Controller Applications

Robust Adaptive Backstepping Motion Control of Underwater Cable-Driven Parallel Mechanism Using Improved Linear Model Predictive Control

Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

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