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Go to Editorial ManagerThis 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.
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.
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.
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.
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.
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.
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.
The 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.