Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Effect of Emergency Interrupt Complexity on the Performance of Adaptive Network Based Fuzzy Inference Traffic Light Control System

ANFIS controller is an advance technique of controlling in Traffic Light Control System (TLCS) which adjusts signal timing parameters in response to real time traffic flow fluctuations. However, the performance of ANFIS controller has not been investigated in an emergency environment. Hence, this paper investigates the effect of emergency lane sensor signal interrupt complexity on the performance of an Adaptive Network Based Fuzzy Inference System (ANFIS) TLCS. The cross roads junction with two lanes per road was considered. One Pedestrian and one Railway lane were considered as emergency lanes. One Traffic Light (TL) was used to control vehicle on each road. ANFIS-TLCS was simulated using graphic user interface tool of the MATLAB. The GUI was simulated for the four different cases of emergency interrupt complexity at some specific simulation periods and the preset number of vehicles for each lane using slide button. Performance of the ANFIS controller was tested for: no, more and most complexity emergency Interrupt cases using Cost Efficiency (CE) as a performance metric. The results obtained showed that ANFIS controller performed differently in all tested cases and worse as the complexity increases but performed relatively equal and better at a higher simulation period regardless of the interrupt complexity. Hence, ANFIS controller is recommended as a better Traffic Light controlling technique regardless of any complexity at the road junction. Keywords: Cross road, Traffic Light, Emergency Interrupt Complexity, ANFIS Object, and Cost Efficienc

PENGENDALI ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) UNTUK PENGATURAN KESTABILAN PADA SISTEM WARD LEONARD

The development of control technology has progressed considerably from classical control to automatic controls to intelligent controls. ANFIS is Adaptive Neuro Fuzzy Inference System one of intelligent control techniques that are capable as well as human capabilities. In this study aims to test the  ability  of  ANFIS  controller  on  Ward  Leonard  system  speed  control.  The  design  of  ANFIS controller and Ward Leonard system to be tested using Matlab and Simulink on Matlab by collecting Maximum Overshoot data and Steady State time on Ward Leonard system condition without controller and with ANFIS Controller controller. Results Ward Leonard system without a controller takes 4.32 seconds for the main driving motors reach steady state and 2.013 seconds for the load motor can reach steady state. The unattended Ward Leonard system has a Maximum Overshoot value of 62.2% on the Main Movement Motor and 78.5% on the Load Motor. The Ward Leonard system with ANFIS controller with a unit feedback system on the main Move Motors can reach a steady state time of4,809 seconds and for the 5.55 seconds required by the Load Motor to reach the steady state. The Ward Leonard system with the ANFIS controller has Maximum Overshoot value of each of the main Driving Motor and the Motor load of 0.3%. Factors that cause steady state time of Ward Leonard system condition with ANFIS controller and condition of Ward Leonard system without controller is ANFIS controller which takes a relatively longer time to muffle error based on membership function of existing ANFIS controller. Keywords: ANFIS controller, Ward Leonard system, Maximum Overshoot, Steady State

Adaptive Neuro Fuzzy Inference System control of active suspension system with actuator dynamics

A hybrid intelligent control technique based on combination of neural network and fuzzy logic will be proposed for hydraulic actuated active suspension system. A half car model will be used for design of Adaptive Neuro Fuzzy Inference System (ANFIS) controller for hydraulic actuated active suspension. The nonlinear behavior of hydraulic system and uncertain parameters in active suspension has increased the difficulty of creating mathematical model for active suspension system. The performance of most of the classical controller depends on nature of mathematical model of system. Hence it is very difficult to create classical controller without mathematical model of a system. Fuzzy logic controller has ability to predict the behavior of system without the need of mathematical model of a system. In this paper, ANFIS controller proposed for active suspension due to its ability to handle actuator dynamics and parameter uncertainty in hydraulic actuator. The simulation carried out for sinusoidal road profile in order to measure the performance of proposed controller. The result of simulation indicates performance of the ANFIS controller for active suspension with actuator dynamics

Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The  propotional-integral (PI) and fuzzy system controllers  are developed., based on designer’s test and error process and experts. The  experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers

Frequency Management Strategies for Local Power Generation Network

This paper presents an intelligent load frequency control technique based on ANFIS controller which is capable to restore system frequency within small fraction of time. Frequency deviations in microgrid occur when the system supply is not sufficient to match the demand. Efforts are required to keep the frequency deviation within acceptable limit. Using vehicle-to-grid technology, where electric vehicles are used as energy storage elements for load frequency control in microgrid. For generating the control action to electric vehicles and energy sources in microgrid, type-2 ANFIS has been employed for quick frequency stabilization in the presence of load and source disturbances. Diesel generator and wind generator are DG sources considered in this paper and electric vehicles are used as energy storage element. Optimal power sharing among the different generating units and electric vehicles is achieved by ANFIS controller. Adaptive nature of ANFIS makes it more suitable and highly robust controller for a complex inter-connected system. Simulation results demonstrate that ANFIS controller is highly efficient as compared to PID controller, fuzzy logic controller, and interval type-2 fuzzy logic controller

Thyristor control series capacitor ANFIS controller for damping oscillations

This study applies Adaptive Neuro Fuzzy Inference System (ANFIS)-based TCSC controller for damping oscillations. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn ANFIS controller is designed to damp out the low frequency local and inter-area oscillations of the Multimachine power system. Direct inverse control techniques are used in the design-of TCSC ANFIS controller which is derived directly from neural networks counterpart’s methodologies of the power system and the controller network to provide optimal damping. By applying this controller to the TCSC devices the damping of inter-area modes of oscillations in a multi-machine power system is handled properly. The effectiveness of the proposed TCSC ANFIS controller is demonstrated on two area four machine power system (Kundur system) which has provided a comprehensive evaluation of the learning control performance. Finally, several fault and load disturbance simulation results are presented to stress the effectiveness of the proposed TCSC controller in a multimachine power system and show that the proposed intelligent controls improve the dynamic performance of the TCSC devices and the associated power networ

ANFIS based Direct Torque Control of PMSM Motor for Speed and Torque Regulation

Nowadays, the Permanent Magnet Synchronous Motors (PMSM) are gaining popularity among electric motors due to their high efficiency, high-speed operation, ruggedness, and small size. PMSM motors comprise a trapezoidal electromotive force which is also called synchronous motors. Direct Torque Control (DTC) has been extensively applied in speed regulation systems due to its better dynamic behavior. The controller manages the amplitude of torque and stator flux directly using the direct axis current. To manage the motor speed, the torque error, flux error, and projected location of flux linkage are employed to adjust the inverter switching sequence via Space Vector Pulse Width Modulation (SVPWM). One of the most common problems encountered in a PMSM motor is Torque ripple, which is recreated by power electronic commutation and a better controller reduces the ripples to increase the drive's performance. Conventional controllers such as PI, PID and SVPWM-DTC were compared with the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of performance measures such as speed and torque ripple. In this work, the Two-Gaussian membership function of the ANFIS controller is used in conjunction with a PMSM motor to reduce torque ripple up to 0.53 Nm and maintain the speed with a distortion error of 2.33 %