Stabilising control for a class of chaotic systems based on adaptive fuzzy logic systems

For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first,based on the universal approximation property of fuzzy logic systems,the Mamdani-type fuzzy logic systems with the parameter adaptive laws are designed utilising the data information sampled from the inputs and outputs of unknown functions in the chaotic systems,then the fuzzy logic systems are used to design the stability controller with three parameter adaptive laws,but the three parameters have no relationship with the number of fuzzy rules,so the stability controller is not only able to achieve asymptotic stabilisation for the chaotic system’s states,but also to reduce the number of fuzzy rules and the no-line computational burden significantly.Finally,simulations are used to show the validity of the stabilisation method.

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.

Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm

In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.

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.