Mamdani and Takagi-Sugeno fuzzy controller design for ship fin stabilizing systems

Chang, Wen‐Jer; Hsu, Feng-Ling

doi:10.1109/fskd.2015.7381966

Cited by 15 publications

(4 citation statements)

References 11 publications

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“…In addition to the above defined linguistic values, we also define very unlikely (U L + ), unlikely (U L), likely (L), more likely (M L), most likely (M L + ) as the linguistic values for the output variable p k . Here, similar to other fuzzy control system applications in the literature (e.g., [10], [31], [35]), we empirically determine the fuzzy rules based on the domain knowledge on MCS. Some rules defined in our fuzzy control system are shown in Table 3.…”

Section: Fuzzy Inference Enginementioning

confidence: 99%

Multi-Task Allocation in Mobile Crowd Sensing With Mobility Prediction

Zhang

2023

IEEE Trans. on Mobile Comput.

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Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker's moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers' historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers' and tasks' spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.

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Section: Fuzzy Inference Enginementioning

confidence: 99%

Multi-Task Allocation in Mobile Crowd Sensing With Mobility Prediction

Zhang

2023

IEEE Trans. on Mobile Comput.

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“…Considering a sea wave causing the ship's rolling motion, the sea waves' main energy centralizes at a low frequency via spectrum theory. For the rolling damp and resilient moments, the nonlinearity of the ship fin stabilizing system is described in [28]. Using the modeling approach, one can find the corresponding T-S fuzzy model as follows:…”

Section: Numerical Examplementioning

confidence: 99%

Passive Stabilization of Static Output Feedback of Disturbed Nonlinear Stochastic System

Huang,

Sun,

et al. 2023

Mathematics

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This paper investigates the Static Output (SO) control issue of the disturbed nonlinear stochastic system, which achieves passivity. Through the application of fuzzy sets and the stochastic differential equation, a Takagi–Sugeno (T-S) fuzzy model with the terms of multiplicative noise and external disturbance can be constructed to describe the considered systems. Furthermore, the Parallel Distributed Compensation (PDC) concept is used to design a fuzzy controller exhibiting an SO feedback scheme structure. To attenuate the effect of external disturbance, the PDC-based SO fuzzy controller is designed to exhibit passivity. During the derivation of some sufficient conditions, a line-integral Lyapunov function is utilized to avoid the conservative term produced using the derivative membership function. Using converting technologies, a stability criterion belonging to Linear Matrix Inequality (LMI) forms is proposed such that the derived conditions are convex hull problems and are solved through an optimization algorithm. Then, the proposed criterion is used to discuss the problem of SO controller design of ship fin stabilizing systems with added disturbance and noise.

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“…Mamdani's method was based on Lotfi Zadeh's 1973 paper on fuzzy algorithms for complex systems and decision processes. Mamdani-type inference uses piecewise linear membership functions, triangular fuzzy numbers (TFN) and trapezoidal fuzzy numbers (TrFN) as shown in Figure 6 (Alazzam & Ahmed, 2018; Chang & Hsu, 2015). Mamdani's Fuzzy Inference system was used in many research papers to design different types of controllers (Lewis, 2013, Alazzam et al, 2013, Alazzam & Ahmed, 2018Alomar & Alazzam, 2018;Goldberg, 1989;Mastacan & Dosoftei, 2018;Simon, 2002).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Utilizing fuzzy logic controller in manufacturing facilities design: Machine and operator allocation

2020

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In this paper, a fuzzy logic controller will be introduced and discussed that can be applied for manufacturing systems. Our proposed control system will utilize fuzzy logic principles to find the optimum number of machines and operators assignment by developing the heuristic relations between the inputs and the outputs of the controller. The project inputs are the desired daily production and the average product processing time by each machine. The production process is assumed to be automated and the routing of products to these machines will be automated and requires negligible time. All machines are also assumed identical in both the functionality (manufacturing process) and the service time. Given the inputs as the number of products and the average service time, the goal of this controller will be to determine the number of the machines and the number of the operators needed to meet the production demand.

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Mamdani and Takagi-Sugeno fuzzy controller design for ship fin stabilizing systems

Cited by 15 publications

References 11 publications

Multi-Task Allocation in Mobile Crowd Sensing With Mobility Prediction

Multi-Task Allocation in Mobile Crowd Sensing With Mobility Prediction

Passive Stabilization of Static Output Feedback of Disturbed Nonlinear Stochastic System

Utilizing fuzzy logic controller in manufacturing facilities design: Machine and operator allocation

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