Dynamic Control of Three-Link SCARA Manipulator using Adaptive Neuro Fuzzy Inference System

This paper presents the comparative analysis on power factor in Vienna rectifier based grid tied wind energy conversion system using ANFIS (Adaptive Neuro Fuzzy Inference System) and FLC (Fuzzy Logic Control) controllers. ANFIS and FLC controllers are implemented to analyse the power factor of grid tied wind energy conversion system (WECS) under a constant wind speed of 12 m/s. Sample size is calculated using Gpower and has 20 samples totally: Based on the obtained result, it is inferred that ANFIS controller (0.8110) than based WECS has better power factor when compared to FLC (0.6530) based WECS. Vienna rectifier based grid tied wind energy conversion system using ANFIS controller gives better power factor when compared to FLC controller.

Most cable manufacturing companies use Programmable Logic Controllers with conventional controllers to control line speed during cable extrusion. These traditional controllers have difficulties keeping the line speed constant, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these challenges, data on the causes of defects during cable manufacturing were collected from a cable manufacturing company in Ghana to ascertain the possible causes during cable manufacturing. Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was designed to provide a constant line speed during the cable extrusion process. To ascertain its robustness, the ANFIS controller was compared to a conventional Proportional Integral Derivative controller and a Fuzzy Logic controller. The controllers were designed and simulated using MATLAB/Simulink software. The analysis of the collected data indicated that a break in insulation/ sheath was a frequently occurring defect during the cable manufacturing process due to improper line speed control of the machines used in the cable manufacturing process. Based on the results obtained from the various controllers, it was concluded that the ANFIS controller was robust in achieving stability regarding line speed variations.

Research of position control of 1-DOF high-precision rotary table using adaptive Neuro-Fuzzy inference system (ANFIS) controller has been done. In the closed-loop system without a controller, the response was oscillating and pounding caused by inertial torque. It because a rotary table receives a considerable load. Based on this, the ANFIS controller is needed to eliminate oscillations and compensate for the inertia. The result shows that there was no oscillation or overshoot with the steady-state error value of 2.27% for the reference angle of 45°, valued at 0.10% reference angle of 180°, and valued at 0% reference angle of 360°. The result proves that ANFIS controllers can eliminate oscillations with and compensate for inertia.

PID motor controllers are largely implemented in industrial applications because of their uncomplicated structure and simplicity in their design. PID controllers are generally preferred where the system characteristics are linear. But when system characteristics are non-linear, we have to change PID control gains according to system requirements. Moreover, the noise in the feedback sensing system makes the PID controller less suitable for system control. This paper aims to provide an advanced approach to improve the dynamic speed response of permanent magnet synchronous motor (PMSM) by replacing the PID controller with an adaptive neuro-fuzzy inference system (ANFIS) controller. This specific technique delivers the benefits fuzzy logic and neural network-based control system.

An adaptive neuro-fuzzy inference system (ANFIS) controller is developed and presented in this study to control hybrid energy storage system (HESS) which combines the battery and super-capacitor (SC). The battery compensates the energy requirement for a longer duration while the SC limits the stress on battery caused by the power fluctuations during transient period which alternately gives longer life span for the battery while regulate the DC link voltage constant. The proposed ANFIS controller is being compared for performance with various other controllers including the reinforcement controller based on Q-learning proportional and integral (PI) controller, fuzzy controller and conventional PI controller. Further, the state of charge (SOC) of the battery and SC are monitored in order to decide the required optimal amount of power or energy for the HESS in deficit/excess modes. The results of the simulation, in different loading conditions, indicate that the ANFIS's controller performance for the DC microgrid is superior compared to others.

ABSTRACTThis paper reveals the impact of an ultra-capacitor (UC) and thyristor control phase shifter (TCPS) on frequency stability of large hybrid interconnected power system. The impact of UC and TCPS has been studied for general purpose hybrid generated multi areas power system. Further to this, an adaptive neuro fuzzy inference system (ANFIS) is is proposed for automatic load frequency control (ALFC). Thermal and reheat thermal plants are connected in Area-1 and area-2, whereas area-3 has hydro plant and area-4, 5, 6 consists of nuclear power plant, diesel power and gas turbine plant, respectively. A micro grid based on Solar Photovoltaic (PV) system and fleet of electric vehicle (EV) system is developed and integrated with load side of area-1 for load management in interconnected grid. Effect of Small load change and large load change are discussed in separate cases. The controllers are tuned by adding sliding surface to enhance the performance. A comparison between ANFIS and PI-based control approaches with and without UC and TCPS exhibits the superiority of ANFIS controller by integrating UC and TCPS. The results of the proposed control technique are compared with already published results.

Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

The control of a dc motor drive system is performed using two Proportional-Integral (PI) controllers, mostly comprising the outer (speed) and inner (torque) loops. However, the tuning of the PI controllers is challenging, because of the significant overshoot and substantial settling time. The overshoot cannot be absolutely removed without sacrificing the speediness. In this paper, an experimental implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) based high-precision controllers for a dc motor drive system is presented for eliminating the overshoot contemporaneously improving the settling time. In this regard, for fair comparison, Bode Plot method is used for obtaining most satisfactory values of Kp and Ki of both the controllers and the performance of the proposed ANFIS controllers is compared with the tuned PI controllers. For the sake of validation, an experimental setup, a 2-quadrant dc motor drive based on DS1104 R&D controller board from dSPACE is used. The designed ANFIS controllers are implemented and also authenticated using simulations. The obtained simulation as well as experimental results indicated that the ANFIS controller successfully eliminates the overshoot and significantly improves settling time. Thereby, outclass the tuned PI controllers by giving experimental results as 0% overshoot and just 0.18 s settling time.

Wireless sensor nodes have huge energy demand for their operations; they are deployed in remote locations for various applications like weather, industrial, satellite, construction, banking, and medical. Sensor nodes require continuous or uninterrupted power supply during their life cycles. When the available renewable power sources are not sufficient to run the system, the batteries are required to deliver a continuous and uninterrupted power supply. The main focus of proposed model is to design and develop a smart dual battery management system along with a hybrid energy harvesting model that can provide reliable and efficient power support to the sensor node. The problem under consideration also focuses on reducing the state of health degradation of batteries by applying a smart battery charging methodology using an ANFIS (adaptive neuro‐fuzzy inference system) controller. The proposed power management system ensures and meets the expected objectives such as switching of power sources, smart battery charging methodology (constant current and constant voltage [CC‐CV]), and dual battery power support using ANFIS controller. The result was obtained through the simulation and hardware prototype of the proposed system work flawlessly to meet the desired objective with partial charging and discharging of batteries for the prevention of battery degradation and also enhance the lifespan of the batteries.

The modified topologies for a asymmetrical cascaded inverter is analyzed with a smaller number of DC voltage sources, power electronic knobs, and power diodes which can generate several levels. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is suggested for asymmetrical modified cascaded inverter topologies to decrease the Total Harmonic Distortions (THD). This technique forbids the changes present in the performance from the modified inverter voltage. The purpose of this research effort is to demonstrate that the significance of ANFIS is to integrate learning to alter the substance of knowledge that meets the learner's needs. The ANFIS model concert calculates using necessary error quantities that display the perfect location required for an improved conviction. For the THD evaluation with and without ANFIS controller, the analysis of thirty-one-level for asymmetrical design was performed. ANFIS controller will be used to obtaining the controlled Root Mean Square (RMS) output from the voltage. The performance of without controller and with ANFIS controlled 31-level asymmetrical cascaded inverter is evaluated and compared with the use of MATLAB/Simulink.

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

Maximum power point tracking (MPPT) for PV (Photovoltaic) systems is provided in this research using artificial intelligence-based control. The design of MPPT system with Anfis Method on the Zeta Converter with DC Load is used to optimize the work of the Photovoltaic which will be used for DC load sources. The MPPT process consists of four main stages, namely module training data, determining input and output data, determining the number and type of membership functions and ANFIS training data. Zeta converter works like a buck boost, which can increase or decrease the voltage which is an advantage in designing systems with very volatile Photovoltaic sources. Zeta Converter is used to get higher efficiency, smaller input and output current ripple values and smaller core losses in the inductor. To improve the efficiency of system performance, An MPPT algorithm for the adaptive neuro fuzzy inference system (ANFIS) that is programmed into a microcontroller controls the zeta converter. ANFIS control is used because the response is faster and more effective. The combined simulation's findings demonstrate that the ANFIS control was successful, and the system can now produce the best possible power from Photovoltaic ipanelsiiniMPPT mode by boosting efficiency by up to 19.96%.

In recent years, artificial intelligence has become increasingly used due to the development of microcontrollers. In this paper, we propose an intelligent technique that employs the adaptive neuro-fuzzy inference system (ANFIS). We use this approach to improve the conventional direct torque control (DTC), which relies on a PI controller for the induction machine, and to enhance the conventional MPPT control based on the Perturb and Observe algorithm. The overall goal is to improve the performance of the photovoltaic pumping system. In this work, we apply ANFIS control to maximum power point tracking (MPPT-ANFIS). Additionally, we simultaneously optimize the efficiency of the DTC by applying ANFIS control (DTC-ANFIS). We present the results by comparing the photovoltaic pumping system using ANFIS control with the conventional photovoltaic pumping system, using MATLAB/Simulink. The results show that ANFIS control significantly improves the photovoltaic system compared to the conventional control, offering excellent dynamic performance of the induction motor and better utilization of photovoltaic solar energy. However, the ANFIS has some drawbacks, such as high computational time consumption and challenges in implementing a database.

This paper presents a comprehensive study of a three-level Unified Power Quality Conditioner (UPQC) controlled by Adaptive Neuro-Fuzzy Inference System (ANFIS) and its simulation in MATLAB/Simulink. The UPQC is designed to enhance power quality by simultaneously mitigating voltage sags, swells, and harmonic distortions while providing voltage regulation. The proposed ANFIS controller leverages the advantages of both fuzzy logic and neural networks, allowing for adaptive learning and improved performance in dynamic environments. Simulation results demonstrate the effectiveness of the ANFIS controller in comparison to traditional Proportional-Integral (PI) and fuzzy controllers. Key performance metrics, including response time, settling time, and steady-state error, are evaluated across various operating conditions. The findings indicate that the ANFIS-controlled UPQC outperforms both the PI and fuzzy controllers in terms of rapid response and robustness, making it a promising solution for advanced power quality management in modern electrical systems. This research contributes to the growing body of knowledge on intelligent control strategies for power quality enhancement, emphasizing the potential of ANFIS in improving the performance of UPQC systems.

The objective of this study is to present an offline control of highly non-linear inverted pendulum system moving on a plane inclined at an angle of 10° from horizontal. The stabilisation was achieved using three different soft-computing control techniques i.e. Proportional-integral-derivative (PID), Fuzzy logic and Adaptive neuro fuzzy inference system (ANFIS). A Matlab-Simulink model of the proposed system was initially developed which was further simulated using PID controllers based on trial and error method. The ANFIS controller were trained using data sets generated from simulation results of PID controller. The ANFIS controllers were designed using only three membership functions. A fuzzy logic control of the proposed system is also shown using nine membership functions. The study compares the three techniques in terms of settling time, maximum overshoot and steady state error. The simulation results are shown with the help of graphs and tables which validates the effectiveness of proposed techniques