Advanced Interval Type-2 Fuzzy Sliding Mode Control for Robot Manipulator

Hwang, Ji-Hwan; Kang, Young-Chang; Park, Jong‐Wook; Kim, Dong Won

doi:10.1155/2017/9640849

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…163 Also an advanced interval type-2 FLC was integrated with a SMC for the purpose of position tracking of a two-RLM, and the proposed controller has the ability to handle uncertain nonlinear MIMO systems. 164,165 Moreover, an adaptive FLC-SMC control scheme was introduced for reference tracking of a two-RLM with few fuzzy rules in order to reduce the computing time. 166 And an adaptive FLC-SMC was optimized by PSO technique in ref.…”

Section: Sliding Mode Controllersmentioning

confidence: 99%

A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

Alandoli

Lee

2020

Robotica

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SUMMARY There is a high demand for developing effective controllers to perform fast and accurate operations for either flexible link manipulators (FLMs) or rigid link manipulators (RLMs). Thus, this paper is beneficial for such vast field, and it is also advantageous and indispensable for researchers who are interested in robotics to have sufficient knowledge about various controllers of FLMs and RLMs as the controllers’ concepts are elaborated in detail. The paper concentrates in critically reviewing classical controllers, intelligent controllers, robust controllers, and hybrid controllers for both FLMs and RLMs. The advantages and disadvantages of the aforementioned control methods are summarized in this paper; it also has a detailed comparison for the controllers in terms of the design difficulty, performance, and the suitability for controlling FLMs or RLMs.

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Section: Sliding Mode Controllersmentioning

confidence: 99%

A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

Alandoli

Lee

2020

Robotica

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“…There have been many studies to solve this robot equilibrium and motion control problem using various control techniques in the literature. Nonlinear control structures with different analyses and designs [10], [11] dual-mode model predictive control [12], vision-based adaptive control [13], sliding mode control [14], adaptive fuzzy control [15], Takagi-Sugeno type Fuzzy Logic Control (FLC) [16], interval Type-2 FLC [17], semiconcave Control Lyapunov Function (CLF) [18] and advanced interval Type-2 Fuzzy sliding mode control [19] are some of them. Over the past decades, Machine Learning (ML) and Fuzzy Logic (FL) based intelligent control systems have been a dominant topic in research, robotics or control societies.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past decades, Machine Learning (ML) and Fuzzy Logic (FL) based intelligent control systems have been a dominant topic in research, robotics or control societies. Because, ML and FL based hybrid intelligent controllers offer a robust nonlinear controller for complicated systems with dynamic uncertainties and functional uncertainty as well as disturbances [16], [19]. The Fuzzy Logic Controllers (FLCs) have been extensively used and successfully applied in control problem of robotic systems where its mathematical model is difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load

Ünlütürk

Aydoğdu

2022

IEEE Access

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In this paper, a novel Machine Learning (ML) based Adaptive Fuzzy Logic-Proportional Integral (AFL-PI) controller was developed for the self-balancing and precision motion control of a two wheeled Underactuated-Mobile Inverted Pendulum (U-MIP) under variable payloads. One of the external disturbances in balance and movement control of the U-MIP is the amount of payload it carries on. To investigate the effectiveness of the proposed controller, a load bar was mounted on top of the U-MIP. The weights of 55gr each can be attached to this bar for variable payloads. The weights on the bar were labeled as three different classes: Low Load (LL), Normal Load (NL) and Heavy Load (HL). Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) models were tested to obtain the highest load class estimation. The highest load classification accuracy was achieved with ANN. Therefore, the ANN model was applied on the U-MIP. The balance performance of the U-MIP was compared by applying the classical FL-PI and ANN based AFL-PI controller on the robot. In order to compare the body tilt angle performance of the U-MIP, the optimal FL-PI parameter in LL was applied for NL and HL conditions without changing. Then, the proposed ANN-based AFL-PI controller was implemented on U-MIP. With the proposed controller, the body tilt angle variation of the U-MIP was improved by %29.42 for NL and %55.62 for HL compared to the classical FL-PI controller. The validity of the proposed controller was proved by real experiments.

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“…Because fuzzy control has the characteristics of global approximation, it can approximate any nonlinear function with any accuracy, 1 so it has attracted the attention of many scholars. In references, [2][3][4] the dynamics of the manipulator is compensated and controlled by the adaptive fuzzy sliding mode controller. However, the adaptive parameters in the fuzzy control are often adjusted too large, which makes other parameters difficult to be in the accurate range and can only be applied to a small number of systems.…”

Section: Introductionmentioning

confidence: 99%

“…(2) The proposed method gets rid of the dependence on the system model. Unlike references, [2][3][4][5][6][7][8] the parameters in the network can often be effectively adjusted, which can well compensate the system dynamics, so as to effectively solve the problem of system modeling. (3) Unlike references, 3,5,7,9,11,12,20 which need to predict the upper bound of the system uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Radial basis function neural network based second-order sliding mode control for robotic manipulator

Chen

Tang

Zhao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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An adaptive second-order sliding mode control method based on RBF neural network is proposed for n-DOF robotic manipulators in the presence of external disturbances. First, RBF neural network is used to approximate the model information. Second, by using adaptive technology to compensate the uncertainty, whose prior knowledge about upper bound is not required. In addition, since the proposed control scheme is continuous, the chattering phenomenon is almost completely eliminated. Finally, the stability and finite time convergence of the proposed method are proved by Lyapunov stability theory. Through the simulation of 2-DOF manipulator and 5-DOF manipulator, the effectiveness and superiority of the control scheme are verified.

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Advanced Interval Type-2 Fuzzy Sliding Mode Control for Robot Manipulator

Cited by 8 publications

References 13 publications

A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load

Radial basis function neural network based second-order sliding mode control for robotic manipulator

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