9,407 research outputs found
New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems
This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use
A genetic algorithm for the design of a fuzzy controller for active queue management
Active queue management (AQM) policies are those
policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the
hosts on the network borders, and the adoption of a suitable control
policy. This paper proposes the adoption of a fuzzy proportional
integral (FPI) controller as an active queue manager for Internet
routers. The analytical design of the proposed FPI controller is
carried out in analogy with a proportional integral (PI) controller,
which recently has been proposed for AQM. A genetic algorithm is
proposed for tuning of the FPI controller parameters with respect
to optimal disturbance rejection. In the paper the FPI controller
design metodology is described and the results of the comparison
with random early detection (RED), tail drop, and PI controller
are presented
Variable structure control with chattering reduction of a generalized T-S model
In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the
state variables.
In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global
approximation and modelling capability of the Takagi-Sugeno (T-S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T-S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T-S model. Simulation results
indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm.
In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved
A new approach to fuzzy estimation of Takagi-Sugeno model and its applications to optimal control for nonlinear systems
An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T-S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters' weighting method is suggested for tuning T-S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the algorithm
A fuzzy data meta training system for ranking hub container terminals
The potential and critical aspects of any transport service can be highlighted through the estimation of appropriate performance indicators of the examined system. Commonly, container terminal analysis is based first on the evaluation and comparison of quantitative parameters that describe the level of service of the terminal and, on the other side by means of performance indicators related to terminal productivity. In this paper a Fuzzy Inference System for evaluation of a synthetic performance indicator is proposed. This tool could help planners and managers in terminals performances analysis and ranking as well as in assessing the effects of possible intervention on the systems. The proposed approach is suitable in the case of hub container ports. In fact this system is characterised by significant uncertainties and it is not always governed by certain rules, rational behaviour, so that it cannot be easily represented by traditional mathematical techniques and models. In our opinion, could be convenient to define the values of the considered parameters by explicitly define them in an approximate way, that is to say by fuzzy sets
Voltage Stabilization of A DC-Microgrid Using ANFIS Controller Considering Electrical Vehicles and Transient Storage
In this paper, we proposed a DC-microgrid with four main elements for Voltage
stabilization. This research also presented a cost function that will guarantee
the lifecycle of the EVs' battery because we use a Super Capacitor to damp the
transient Ripples of Bus Voltage. This DCMG has four main branches: Ballast,
Random Load, Random Source, and Stabilizer. The Random Source is photovoltaic,
and the Random Load includes consumers. The three first branches make the DCMG
go to the destabilization mode, and the last one has to stabilize its role in
this DCMG. The controller consists of a fuzzy inference system optimized using
PSO (Particle Swarm Optimization) algorithm, so this controller adjusts the
duty cycle of three main branches in the stabilization branch of this DCMG. It
is a MIMO ANFIS controller, and we compared the results of this controller with
other controllers. In this research, we have designed three scenarios to verify
the results: production more than consumption, vice versa, and equality between
production and consumption. In this paper, the efficiency of this method --
using ANFIS controller -- in comparison with others -- using another type of
controller -- will evaluate under different operating conditions, production
and consumption inequality, and equality.Comment: 19 pages, 27 figure
Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension
Scientists must understand what machines do
(systems should not behave like a black box), because in
many cases how they predict is more important than what
they predict. In this work, we propose a new extension of
the fuzzy linguistic grammar and a mainly novel interpretable
linear extension for regression problems, together
with an enhanced new linguistic tree-based evolutionary
multiobjective learning approach. This allows the general
behavior of the data covered, as well as their specific
variability, to be expressed as a single rule. In order to
ensure the highest transparency and accuracy values, this
learning process maximizes two widely accepted semantic
metrics and also minimizes both the number of rules and
the model mean squared error. The results obtained in 23
regression datasets show the effectiveness of the proposed
method by applying statistical tests to the said metrics,
which cover the different aspects of the interpretability of
linguistic fuzzy models. This learning process has obtained
the preservation of high-level semantics and less than 5
rules on average, while it still clearly outperforms some of
the previous state-of-the-art linguistic fuzzy regression
methods for learning interpretable regression linguistic
fuzzy systems, and even to a competitive, pure accuracyoriented
linguistic learning approach. Finally, we analyze a
case study in a real problem related to childhood obesity,
and a real expert carries out the analysis shown.Andalusian Government P18-RT-2248Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities PI20/00711Spanish Government PID2019-107793GB-I00
PID2020-119478GB-I0
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