Fuzzy Adaptive Strong Tracking Cubature Kalman Filter

To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).

Sensorless Control of Switched Reluctance Motor Drive Systems

This paper presents a sliding mode observer for sensorless operation of SRM （switched reluctance motor） drive. Design of such an observer depends mainly on the nonlinear model of SRM. In this technique, neither extra hardware nor huge memory space are not required but it only requires active phase measurements. Furthermore, PI （proportional integral） and adaptive FLPI （fuzzy logic PI） controllers are suggested to operate individually along with the SMO （sliding mode observer） to cover a full speed range of sensorless controller. Both controller schemes operate in PWM （pulse width modulation） control mode. The proposed observer is implemented and tested using a digital signal processor. All results obtained with both simulation and experimental investigations corroborate the superior performance of the adaptive fuzzy logic controller （FLPI） when compared with those of PI controller.

Experimental validation of double-fed induction generator in oscillating water column using circulatory system-based optimization

This paper validates the optimal operation for a grid-connected double-fed induction generator(DFIG)in an oscillating water column power plant(OWCPP).This study presents a novel optimization technique called the circulatory system-based optimization(CSBO)approach to develop six adaptive fuzzy logic controllers(AFLCs)with 30 parameters and compare them to chaotic-billiards optimization(C-BO)and genetic algorithm(GA).The proposed controller is also compared with a proportional-integral differential(PID)controller based on a self-adaptive global-best harmony search(SGHS).CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104.The results of using CSBO-AFLCs revealed a fast time response,fast convergence,less overshoot and minimal error compared with those achieved with C-BO-AFLC,SGHS-PID and GA-AFLC during different case studies.The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors.The results show that the CSBO shows better power tracking by 25%as compared with C-BO,by 45%when compared with the GA and by 56%when compared with PID.Moreover,the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO-AFLC.The root mean square of the errors of six controllers using CSBO is improved by 68.27%when compared with GA,by 22.57%when compared with C-BO and by 38.42%when compared with PID.These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.