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M-estimation as generalization of maximum likelihood estimation (MLE) method is well-known approach to get the robust estimations of location and scale parameters in objective function ρ especially. Maximum logq likelihood estimation (MLqE) method uses different objective function called as ρlogq. These objective functions are called as M functions which can be used to fit data set. The least informative distribution (LID) is convex combination of two probability density functions f0 and f1. In this study, the location and scale parameters in any objective functions ρlogq, ρlog and ψlogq (f0, f1) which are from MLE, MLqE and LIDs in MLqE are estimated robustly and simultaneously. The probability density functions which are f0 and f1 underlying and contamination distributions respectively are chosen from exponential power (EP) distributions, since EP has shape parameter σ to fit data efficiently. In order to estimate the location µ and scale σ parameters, Huber M-estimation, MLE of generalized t (Gt) distribution are also used. Finally, we test the fitting performance of objective functions by using a real data set. The numerical results showed that ψlogq (f0, f1) is more resistance values of estimates for µ and σ when compared with other ρ functions. [ABSTRACT FROM AUTHOR]

In this study, a non-singleton fuzzy sliding control based strategies are investigated with simulation and also experimental studies in order to minimize angular velocity ripples of the nonlinear four-bar mechanism when it is driven by an electric motor. The mathematical model of the full system included the motor and four-bar mechanism is first obtained and open loop reply of the system is illustrated to show angular velocity ripples of the crank in the presence of the constant potential source. Secondly, an optimized PID controller by using pattern search is designed to reduce crank angular velocity ripples for the closed loop system. A new non-singleton type- 1 fuzzy sliding controller is designed in order to obtain stable crank angular velocity in the steady state and performances of different types of fuzzy sliding controllers are comparatively illustrated. In addition to simulation results, experimental results of the controlled systems are also presented in order to show the effectiveness of the controllers in practice. As far as the industrial applications are concerned, simpler and more practical control algorithm is obtained with non-singleton type-1 fuzzy sliding control structure. [ABSTRACT FROM AUTHOR]

In this paper, a new controller based on type-2 fuzzy logic is designed for a four-bar mechatronic system. The main purpose of the study is to reduce crank angular speed fluctuations of the four-bar mechanism driven by an electrical motor. Firstly, the mathmetical model of the system is derived and then a robust type-2 fuzzy logic controller is designed. Inputs and output of the type-2 fuzzy control systems are characterized by type-2 membership functions. The simulation results are presented in the paper in order to show reduction of the crank angular speed fluctuations. [ABSTRACT FROM AUTHOR]