Sliding mode fuzzy control for Takagi–Sugeno fuzzy systems with bilinear consequent part subject to multiple constraints

Chang, Wen‐Jer; Hsu, Feng-Ling

doi:10.1016/j.ins.2015.08.026

Cited by 43 publications

(23 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For AI technology, research on deep reinforcement learning (DRL) algorithm applied to course control of underactuated ships [65]. In addition, the prediction algorithm combined with the sliding mode algorithm [73] or the neural network algorithm [74], the fuzzy control algorithm combined with the sliding mode algorithm [75] or the neural network algorithm [76], ADRC combined with the fuzzy control [77] or support vector machine(SVM) [78] also have better control of the course of different types of ships under different conditions.…”

Section: Course Controlmentioning

confidence: 99%

“…Fuzzy control can also be combined with other algorithms. For example, it can be combined with predictive control [80] or sliding mode control to solve multi-performance constrained sliding mode control problems [75]. Or we can integrate dynamic surface control (DSC) technology and minimal learning parameter(MLP) method to construct an approximation control system of the T-S fuzzy system [94].…”

Section: Fuzzy Logic Control Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Wang

Liu

et al. 2019

JMSE

View full text Add to dashboard Cite

At present, with the development of waterborne transport vehicles, research on ship faces a new round of challenges in terms of intelligence and autonomy. The concept of maritime autonomous surface ships (MASS) has been put forward by the International Maritime Organization in 2017, in which MASS become the new focus of the waterborne transportation industry. This paper elaborates on the state-of-the-art research on motion control of MASS. Firstly, the characteristics and current research status of unmanned surface vessels in MASS and conventional ships are summarized, and the system composition of MASS is analyzed. In order to better realize the self-adaptability of the MASS motion control, the theory and algorithm of ship motion control-related systems are emphatically analyzed under the condition of classifying ship motion control. Especially, the application of intelligent algorithms in the ship control field is summarized and analyzed. Finally, this paper summarizes the challenges faced by MASS in the model establishment, motion control algorithms, and real ship experiments, and proposes the composition of MASS motion control system based on variable autonomous control strategy. Future researches on the accuracy and diversity of developments and applications to MASS motion control are suggested.

show abstract

Section: Course Controlmentioning

confidence: 99%

Section: Fuzzy Logic Control Algorithmmentioning

confidence: 99%

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Wang

Liu

et al. 2019

JMSE

View full text Add to dashboard Cite

show abstract

“…In the first category, the mismatched uncertainties are assumed to be bounded with H 2 norm. The stability of the systems with mismatched uncertainties have been examined using the Riccati difference equation, adaptive, game theoretic, fuzzy control and linear matrix inequality (LMI)-based approaches (Abo-Hammour et al 2014; Arqub and Abo-Hammour 2014; Chang 2009; Chang and Hsu 2016; Chang et al 2015; Choi 2007; Park et al 2007). However, this is not a realistic assumption in practice since mismatched uncertainties may have non-zero steady-state values.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

Kayacan

2018

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper presents a novel sliding mode control (SMC) algorithm to handle mismatched uncertainties in systems via a novel self-learning disturbance observer (SLDO). A computationally efficient SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a neuro-fuzzy structure (NFS) work in parallel. In this framework, the NFS estimates the mismatched disturbances and becomes the leading disturbance estimator while the former feeds the learning error to the NFS to learn system behaviour. The simulation results demonstrate that the proposed SMC based on SLDO (SMC-SLDO) ensures robust control performance in the presence of mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC) and SMC based on a basic nonlinear disturbance observer (SMC-BNDO), and also remains the nominal control performance in the absence of mismatched uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched time-varying uncertainties, but also improve the transient response performance in the presence of mismatched time-invariant uncertainties. Moreover, the controller gain of the SMC-SLDO is required to be selected larger than the upper bound of the disturbance estimation error rather than the upper bound of the actual disturbance to guarantee system stability, which results in eliminating the chattering effects on the control signal.

show abstract

“…He took the uncertain objects as the research object. It mainly contented the fuzzy mathematics [8][9], fuzzy logic [10][11], fuzzy system [12][13], fuzzy control [14][15] and fuzzy decision [16][17]. TOPSIS method is short for technique for order preference by similarity to ideal solution.…”

Section: Introductionmentioning

confidence: 99%

Research on the Comprehensive Strength Evaluation for Universities based on the Fuzzy TOPSIS Method

Yue

2017

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Abstract-In recent years, the scale of enrollment in colleges and universities continues to expand. The private colleges are also springing up. The comprehensive strength of each university is uneven. Evaluating the comprehensive strength for the colleges is not only to provide the reference for the majority students' parents, but also to promote the continue development for the comprehensive strength of the colleges. In this paper, in order to make the better evaluation of the comprehensive strength for the colleges, we combine the fuzzy theory with the TOPSIS method and propose an improved fuzzy TOPSIS method. Then, we use the method to evaluate the comprehensive strength for the college. The experimental results demonstrate the reliability and the validity of the method.

show abstract

Sliding mode fuzzy control for Takagi–Sugeno fuzzy systems with bilinear consequent part subject to multiple constraints

Cited by 43 publications

References 35 publications

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

Research on the Comprehensive Strength Evaluation for Universities based on the Fuzzy TOPSIS Method

Contact Info

Product

Resources

About