Adaptive Fuzzy Control of a Cable-Driven Parallel Robot

Vu, Mai The; Hsia, Kuo-Hsien; El-Sousy, Fayez F. M.; Rojsiraphisal, Thaned; Rahmani, Reza; Mobayen, Saleh

doi:10.3390/math10203826

Cited by 12 publications

(3 citation statements)

References 45 publications

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“…However, in practice, higher precision and smoother control inputs are essential. To mitigate this issue, various techniques have been developed, including approximation methods such as neural networks (NNs) [10] or fuzzy logic systems (FLSs) [11,12], observerbased controller [13], high-order SMC (HOSMC) [14], and the boundary method (BM) [15], among others. Furthermore, it is important to note that the system must be stable within a finite-time frame rather than achieving exponential stability as in various works such as [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

Truong

Kang

2023

Mathematics

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This paper proposes a fixed-time neural network-based prescribed performance control method (FNN-PPCM) for robot manipulators. A fixed-time sliding mode controller (SMC) is designed with its strengths and weaknesses in mind. However, to address the limitations of the controller, the paper suggests alternative approaches for achieving the desired control objective. To maintain stability during a robot’s operation, it is crucial to keep error states within a set range. To form the unconstrained systems corresponding to the robot’s constrained systems, we apply modified prescribed performance functions (PPFs) and transformed errors set. PPFs help regulate steady-state errors within a performance range that has symmetric boundaries around zero, thereby ensuring that the tracking error is zero when the transformed error is zero. Additionally, we use a singularity-free sliding surface designed using transformed errors to determine the fixed-time convergence interval and maximum allowable control errors during steady-state operation. To address lumped uncertainties, we employ a radial basis function neural network (RBFNN) that approximates their value directly. By selecting the transformed errors as the input for the RBFNN, we can minimize these errors while bounding the tracking errors. This results in a more accurate and faster estimation, which is superior to using tracking errors as the input for the RBFNN. The design procedure of our approach is based on fixed-time SMC combined with PPC. The method integrates an RBFNN for precise uncertainty estimation, unconstrained dynamics, and a fixed-time convergence sliding surface based on the transformed error. By using this design, we can achieve fixed-time prescribed performance, effectively address chattering, and only require a partial dynamics model of the robot. We conducted numerical simulations on a 3-DOF robot manipulator to confirm the effectiveness and superiority of the FNN-PPCM.

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Section: Introductionmentioning

confidence: 99%

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

Truong

Kang

2023

Mathematics

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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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“…In practical engineering, uncertainties and nonlinearities are commonly present, making it challenging to obtain precise mathematical models for these systems. Therefore, methods like adaptive fuzzy control [13] and neural network control [14], which do not rely on exact mathematical models, are better suited for designing controllers for unknown nonlinear systems. Combining these methods with backstepping has resulted in numerous research achievements.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone

Zhao,

Chang,

2023

Applied Sciences

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In this paper, an adaptive fuzzy tracking control method is proposed to address the issues of dead-zone and unobservable states in a flexible robotic arm system. The control design process begins with the utilization of a fuzzy logic system to approximate the nonlinear functions present in the flexible robotic arm system. To estimate the unobservable states of the system, a state observer is then designed. To alleviate the computational complexity during controller design, the command filtering technique is introduced. Additionally, the Nussbaum function is incorporated to address the unknown control gain problem. The stability of the system can be verified through the design of a Lyapunov function. This study’s simulation results demonstrate that the designed control system can closely track the specified reference signals. The closed-loop system effectively controls the flexible robotic arm, as verified through experimentation.

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Adaptive Fuzzy Control of a Cable-Driven Parallel Robot

Cited by 12 publications

References 45 publications

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

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

Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone

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