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Go to Editorial ManagerThe Mobile Manipulator Robot (MMR) has many applications in different aspects of the life, for example, grasping and transporting, mining, military, manufacturing, construction and others. The benefits of MMR rise in dangerous place where the human cannot reach such as disaster areas and dangerous projects sites. In this work, the PID controller is combined with Fuzzy Logic Controller (FLC) to structure the Fuzzy Supervisory Controller (FSC) to overcome the drawbacks of PID controller and to obtain the advantages of FLC. Two approaches are suggested for the navigation of Autonomous Mobile Robot (AMR). These are; goal reaching fuzzy control (GRFC) and the obstacle avoidance fuzzy control (OAFC). The hardware implementation of the AMR is performed using AVR ATmega32 microcontroller, two DC motors, light dependent resistor (LDR) and five Infra Red sensors. While, the Laboratory robot arm with some fabrications is used as manipulator arm with a five degrees-of- freedom. Then a microcontroller is employed to implement the proposed controller for MMR. The designed MMR is tested in real environments and give a good navigation.
This paper presents a novel control framework for robot manipulator path tracking based on the integration of artificial immune systems, fuzzy logic, and fractional-order PID control. The proposed Fuzzy-Immune Fractional-Order PID (FIFOPID) controller combines immune feedback mechanisms, fuzzy logic reasoning, and fractional-order control principles, with controller parameters optimized using the Clonal Selection Algorithm (CSA). The performance of the FIFOPID controller is evaluated and compared against a Fuzzy-Immune PID (FIPID) controller under identical conditions. Simulation results conducted in MATLAB 2014a with SIMULINK demonstrate that the optimal FIFOPID controller outperforms the FIPID controller in terms of path tracking accuracy and overall control performance, highlighting its potential as an effective approach for precise robotic manipulator control.
In this paper, a neuro-fuzzy network-based adaptive tracking controller is suggested for controlling a type of nonlinear system. Where two neuro-fuzzy networks have been used to learn the system dynamics uncertainty bounds by using Lyapunov method. Then the output of these two networks are used to build a sliding mode controller. The stability of the control system is proved and stable neuro-fuzzy controller parameters adjustment laws are selected using Lyapunov theory. Simulation case study shows that the controlled system tracking the reference model effectively with smooth control effort and robust performance has been achieved.
This paper proposes a fuzzy logic based controller for boost type DC/DC converter. It forms an improvement to the dynamic performances of the well known PI like fuzzy controller which uses the output voltage error & its rate of change as an inputs. The proposed controller generates a duty ratio control signal through the addition of a weighted part of the input voltage and of the low pass filtered signal of the inductor current to that of the fuzzy controller which is fed by voltage error and a signal representing the differences of the output voltage from its low pass filtered version. The controlled boost DC/DC converter exhibited excellent performances under small and larger disturbances of the input voltage and output load resistance and also showed good reference tracking ability.
This paper is concerned with the design of a new controller for active suspension system. The model is considered as a quarter-car. The presented controller depends on the fuzzy technique and NARMA-L2 linearization algorithm. The compensation system that added by the fuzzy rules improves the performance of the controller, while the neural network produces the required control signal. The new controller can achieve an improvement of the ride comfort with a reasonable value of power consumption. The mathematical analysis of the mechanical power used by the model is focused on the average and the RMS of the power supplied to the system, regardless of the frequency content of the vibration signal. The simulation results which are verified by a practical examples of road profiles, demonstrate the efficacy of the proposed controller.
Recently, Internet of Thing technology has been used to develop numerous applications, this paper compromising design and implementation of greenhouse prototype that integrated with the IoT to adjust the system’s parameters and monitor the system status from any place in this world. This system involves three intelligent controllers that designed to stabilize the temperature degree, water level in soil, and light intensity inside the greenhouse prototype structure. These systems have been built by two important parts: the hardware and software. The hardware part could be achieved by designing and implementing the control circuits, actuators, and install the sensors as well as the devices. The second one is the software part which is involves implementing Fuzzy Inference Engine that represent the system’s brain that monitor and manage the entire process in the system to ensure the best performance. This system has been built to contain three control systems that means there are three different Fuzzy controllers. In order to keep the system practicality, the fuzzy controllers should be aggregated in single code that resides in single microcontroller chip with additional codes that perform the IoT duties. The proposed IoT system provides the ability for specific people to monitor and manage their systems remotely, using a web application with cloud technology. The major contributions of the proposed system are started by downloading the controller’s set-points (the desired environmental conditions) from the web page, transfer the set- points to the controllers, and upload data that read from sensors to the same web page.)
In this paper, a second order Sliding Mode Controller (SMC), based on Super – Twisting algorithm, Fuzzy estimator and PID controller is presented for quarter vehicle active suspensions. Because of the chattering that appeared at the output of the system when using first order SMC, second order SMC is preferred. The proposed controller has been derived in order to achieve the convergence and the stability of the system that can improve the comfortable driving and vehicles safety against different road disturbances. The Artificial Bee Colony optimization method has been utilized to find the optimal values of the proposed controller parameters. The obtained results of the simulations have been verified the efficiency and the ability of the proposed control scheme to suppress the oscillations and give the stability of the suspension system in the presence of uncertainty and different road disturbances.
An intelligent and anticipatory speed controller for internal combustion engines was designed theoretically and examined experimentally. This design was based on the addition of a torque loop to the main speed loop. The model can sense the external load with the help of a load cell and send this signal to a soft computing unit for analysis and processing. This scheme will improve the ability of anticipation of controller since it treats the factors that affect the speed, not the speed itself. The experimental design was implemented using two types of actuating techniques; an intelligent throttling actuator and an intelligent injection actuator. The signal was analyzed by using intelligent techniques such as fuzzy logic, neural network and genetic algorithm. The experimental data were used to train the neural and the Adaptive Neuro–Fuzzy Inference System. The comparison of the results obtained in this work with other available models proved the efficiency and the robustness of the present model.
The aim of this work is to experimentally study the influence of fiber prestress and curing temperature on the tensile and flexural properties of carbon fiber-epoxy composite. Adaptive Neuro-Fuzzy Inference System model was used to predict the effect of fiber prestress and curing temperature on the tensile strength, tensile modulus, flexural strength and flexural modulus of carbon fiber-epoxy composite. It was found that, the best membership functions for predicting the tensile strength, tensile modulus and flexural modulus are Gaussian membership functions with 4 number of membership function, and for predicting the flexural strength are generalized bell membership functions with 4 number of membership functions. From the comparison between the experimental and predicted results of carbon fiber-epoxy composite properties, it is found that the prediction results of this model show a good agreement with experimental results.
Cascade multilevel inverter is a power electronic device built to synthesize a desired ac voltage from several levels of dc voltages. Such inverters have been received increasing attention in the past few years for high power application. A small total harmonic distortion is the most important feature of these inverters. Cascade multilevel inverter is used in this work with proposed control circuit to control the output voltage using sinusoidal pulse width modulation (SPWM). PD-like Fuzzy+I controller is used to control this system to get the required output voltage. The results gained in this work prove the validity of the proposed controller of having an output voltage with minimum distortion.
A five-phase two-motor drive system with a series connection of stator windings and decoupled dynamic control is considered in the present paper. The two-motor drive system is supplied from a single five-phase Space Vector Pulse Width Modulation (S VPWM) Voltage Source Inverter (VSI) and controlled using a vector control scheme, provided that the stator windings are connected in series with appropriate phase transposition. The concept has been developed under the assumption that the inverter voltages are controlled in the stationary dq-reference frame. A fuzzy logic-based speed controller has been constructed and used to drive the two-motor in this work. The two-motor system, inverter system, and fuzzy controller models are implemented and tested using Simulink/Matlab facilities. 1be presented results show the validity of the model to do well for the sake of speed control in wider different operating conditions.
Among the soft-switching techniques, the Zero-Current Zero-Voltage Transition (ZCZVT) technique is used in this paper. It is based on the Resonant Transition Mechanism requirements, which permit newcomers to perceive the Resonant Transition techniques as a whole instead of dissimilar soft-switching techniques. The open loop operation of the power circuit (DC/DC Boost Converter) and control circuit have been implemented and tested with MatLab software. The simulation test facility and the analytical development tools being used are described. The derivation of closed loop control strategy based on fuzzy logic control with nonlinear fuzzy sets for input and output variables is described in detail. The closed loop simulation results that describe the performance of the proposed converter with this control strategy due to different effects are also included.
Obstacle avoidance and path planning are from the most important problems in mobile robots, especially in unknown environment . In this paper, we proposed an approach for mobile robot navigation combining path planning and obstacle avoidance. Methods such as obstacle avoidance are inspired from the nature, and have been developed by fuzzy logic to train an intelligent robot in unknown environment. The model of the robot has two driving wheels and the linear velocity and azimuth of the two wheels are independently controlled using PID controller. Inputs are obtained from ultrasonic sensors mounted on it.
This paper introduces a radial distribution feeder protection scheme based on certain features extraction from current signals measurement at the substation. The features are captured using the discrete wavelet transform (DWT). Two digital signals processing methods are used to introduce those features to the 1) fault detection 2) identification and 3) localization schemes; the first one is the energy method and the second one is the root mean square method. For the purpose of fault type identification, two systems are tested and compared, a Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Fault location scheme is then built based on ANNs. An effort is made to reduce the computational burden and the speed of detection provided by the fault detection and identification schemes. Since the short circuit faults are the most likely types of faults that can occur in power systems, the ten types of these faults taking into account different fault resistances are simulated in MATLAB environment and the protection scheme is built based on the idea of over current. The power quality disturbances such as switching transient events on the feeder is also taken into account in order to build a reliable and secure protection scheme.
The excitation and governing control of generator play an important role in improving the dynamic and transient stability of power system. Typically the excitation control and governing control are designed independently. This paper, presented Neuro-Fu;,.zy methods for the excitation and governing control . Neuro-Fuz.zy system is applied to generate two compensating signals to modify the controls dwing system disturbances. A single machine to infinite bus (SMIB) system is applied in simulation. The MATLAB SIMULIK and S-function technique is used to simulate the system and controllers