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The research work carried out in this paper presents a novel intelligent tool condition monitoring solution for the turning process using an enhanced adaptive neural fuzzy inference system based on extended subtractive clustering. The hybrid system is constructed from training a takagi-sugeno-kang fuzzy logic system by integrating machining parameters- feed, cutting force, feed force and cutting tool...
This paper addresses a high precision discrete-time model-free PID adaptive neuro-fuzzy logic motion controller in case the physical models that describe a robot are not known. The advantage of this kind controller is that it uses an improved subtractive clustering technique to obtain the structure of the system model in order to ensure the high accuracy of the intelligent control. Moreover, the information...
This paper proposes a model-free PID fuzzy logic control for parallel robot. This kind control differs from conventional classical and modern control techniques, even existed intelligent controls. Nor precise description of dynamics model neither physical parameter is required for construction of the fuzzy control. Takagi-Sugeno-Kang (TSK) fuzzy approach with extended subtractive clustering computing...
Many modern and intelligent control methods had been developed for nonlinear systems in order to get better motion accuracy and dynamic performance for parallel robot. This paper aims to propose a nouvelle model-free adaptive neural fuzzy feed forward torque control for parallel mechanism. The advantage of this kind model-free control is that it uses the information directly from the nonlinear dynamics,...
The Inver-over operator is always stuck to local optima in solving the Traveling Salesman Problem(TSP). In this paper, two improved Inver-over operators are proposed which contain the noise method(NM) based local search with multiple different neighboring structures. An effective memetic algorithm(MA) based on two improved Inver-over operators is implemented, which conduct different operators in different...
Cutting forces prediction is very important in micromilling for cutting tool's design and process planning. This paper presents a new model for uncertainty estimation of dynamic cutting forces in micromilling using a type-2 fuzzy rule-based system. The type-2 fuzzy estimation not only filters the noise and estimates the instantaneous cutting force in micromilling using observations acquired by sensors...
In this paper, type-2 fuzzy logic system is applied to analyse acoustic emission signal feature for tool condition monitoring in a tool micromilling process. To make the comparison and evaluation of AE signal features easier and more transparent, Type-2 fuzzy analysis is used as not only a powerful tool to model AE SFs, but also a great estimator for the ambiguities and uncertainties associated with...
Because of the difficulty in understanding the physics of the machining process, several different intelligence methods, which employ cutting forces for estimation tool wear, have been developed in the past few years. Unfortunately, none of them can overcome the difficulty to estimate the errors of approximation during tool wear monitoring. This paper aimed at presenting a tool wear monitoring method...
In this paper, the joint friction of a robotic manipulatoris identified by using subtractive clustering based Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). The proposed approach can provide accurate prediction of the joint friction despite the nonlinearity of the friction and measurement uncertainty. Simulation results show the effectiveness and convenience of the method.
This paper presents the generalized type-2 Takagi- Sugeno-Kang (TSK) fuzzy logic system (FLS) in which the antecedent or consequent membership functions are type-2 fuzzy sets and the consequent part a first or higher order polynomial function. The architecture of the generalized type-2 TSK FLS and its inference engine are based on the Mendel's first order type-2 TSK FLS. The design method of high...
In this paper, a subtractive clustering based type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic process is used as a fuzzy filter to treat acceleration data for the purpose of obtaining the rigid-body dynamical parameters of robotic manipulators. Experimental results show the effectiveness of this method, which not only provides good accuracy of prediction of the rigid-body dynamical parameters of robotic...
In this paper, a subtractive clustering identification algorithm is introduced to model type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic systems (FLS). The type-2 TSK FLS identification algorithm is an extension of the type-1 TSK FLS modeling algorithm proposed in (S. L. Chiu, 1994), (S. L. Chiu, 1997). In the type-2 algorithm, subtractive clustering method is combined with least squares estimation algorithms...
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