Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints

Sun, Kangkang; Mou, Shaoshuai; Qiu, Jianbin; Wang, Tong; Gao, Huijun

doi:10.1109/tfuzz.2018.2883374

Cited by 310 publications

(81 citation statements)

References 48 publications

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“…Fuzzy control [25,26] Computer intelligent control based on fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning. The input of this mechanism is to change the original 0 and 1 data into a value between 0 and 1 through fuzzification, which is closer to human thinking than the original nonzero or one dichotomy.…”

Section: Description Advantage Disadvantagementioning

confidence: 99%

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Chang¹,

Chu²,

Lin³

et al. 2021

Automation and Control

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Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people's way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice.

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Section: Description Advantage Disadvantagementioning

confidence: 99%

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Chang¹,

Chu²,

Lin³

et al. 2021

Automation and Control

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show abstract

“…The features of legless mode are: i) primary motion mode, ii) moved by the internal reaction force, iii) legs are folded inside the robot body, iv) no external moving parts, v) hermetically sealable, vi) any suitable outer structure is possible, and vii) suitable for applications inside the human body. In the legless mode, the robot is an underactuated system and nonlinear [19,20] and, a behaviour based control has been presented in [18] to control this nonlinear underactuated system.…”

Section: Legless Modementioning

confidence: 99%

Modelling and Motion Analysis of a Pill-Sized Hybrid Capsule Robot

Huda

Liu

Saha

et al. 2020

J Intell Robot Syst

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This paper presents a miniature hybrid capsule robot for minimally invasive in-vivo interventions such as capsule endoscopy within the GI (gastrointestinal) tract. It proposes new modes of operation for the hybrid robot namely hybrid mode and anchoring mode. The hybrid mode assists the robot to open an occlusion or to widen a narrowing. The anchoring mode enables the robot to stay in a specific place overcoming external disturbances (e.g. peristalsis) for a better and prolonged observation. The modelling of the legged, hybrid and anchoring modes are presented and analysed. Simulation results show robot propulsions in various modes. The hybrid capsule robot consisting four operating modes is more effective for the locomotion and observation within GI tract when compared to the locomotion consisting a single mean of locomotion as the hybrid robot can switch among the operating modes to suit the situation/task.

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“…Another approach would have consisted in using Fuzzy Logic (FL) [19,20]. Yet another approach could be using the Filippov solutions [21].…”

Section: Stability Analysismentioning

confidence: 99%

Simulating a syringe behavior using a pneumatic cylinder haptic interface

Senac

Lelevé

Moreau

et al. 2019

Control Engineering Practice

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Mastering medical gestures, such as epidural needle insertion, requires much practice. Haptic interfaces can be efficient training solutions. Nevertheless, they must provide users with an accurate experience for an effective training. This article introduces a control framework that turns a pneumatic cylinder into a haptic interface able to simulate a syringe and more specifically the loss of resistance phenomenon encountered during an epidural needle insertion. To achieve this, the framework switches between two control laws (position and force tracking) according to the system state. Experiments involving two anesthetists confirm the practicality of this system for hands-on training or rehearsal purposes.

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Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints

Cited by 310 publications

References 48 publications

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Modelling and Motion Analysis of a Pill-Sized Hybrid Capsule Robot

Simulating a syringe behavior using a pneumatic cylinder haptic interface

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