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Go to Editorial ManagerVehicles usually consist of several essential systems. The performance of the vehicle is evaluated through the efficiency of these systems to perform their duties. The suspension system is one of these systems dedicated to absorbing shocks arising from vehicles passing over road bumps, thus reducing vibrations and achieving passenger comfort while driving. This paper presents a study on enhancing ride comfort in a nonlinear half-car model using a modified Proportional-Integral-Derivative (PID) controller. In this study a half-car model is developed considering the nonlinearities in the suspension system components. A nonlinear half-car model was adopted to increase accuracy and make the overall system closer to reality. Instead of the feed-forward conventional PID controller gains, the proposed controller gains are formed by putting the proportional and derivative gains in the feedback path while keeping the integral gain in the feed-forward path to act as an I- PD controller. The proposed controller is integrated into the model to deal with these nonlinearities effectively and to achieve the optimal performance of the vehicle body. The overall system has been developed and simulated in the Matlab Simulink environment to show the dynamic response. Simulation results demonstrate the effectiveness of the I-PD controller in improving the ride comfort and handling stability of the nonlinear half-car model by reducing body acceleration and suspension deflection. A comparison with other study has been conducted to verify the effectiveness of the proposed controller.
The purpose of this research is to control a quarter car suspension system and also to reduce the fluctuated movement caused by passing the vehicle over road bump using modified PID (Proportional Integral and Derivative) controller. The proposed controller deals with dual loop feedback signals instead of single feedback signal as in the conventional PID controller. The structure of the modified PID controller was created by moving the proportional and derivative actions in the feedback path while remaining the integral action in the forward path. Thus, high accuracy results were obtained. Firstly, modelling and simulation of linear passive suspension system for a quarter car system was performed using Matlab – Simulink software. Then the linear suspension system was activated and simulated by using an active hydraulic actuator to generate the necessary force which can be regulated and controlled by the proposed controller. The performance of whole system has been enhanced with a modified PID controller.
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.
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.
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.
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.
This paper deals with the modeling and control strategics of the motion of wheeled mobile robot. The model of the mobile robot has two driving wheels and the azimuth and velocity are dependently controlled by two PID controllers. The PID controller is one of the earliest and famous industrial controllers. It has many advantages: It is economic, simple easy to be tuned and robu.~t. The tuning of these controllers is governed by system nonlinearities and continuous parameter variations. lbis paper deals with the optimal design of a PID controller for path tracking of mobile robot by using genetic algorithms (GA). The designed controller is tested for different paths.
This study presents a speed control design for switched reluctance motor (SRM) drive based on PID controller. The applications of Switched Reluctance Motors (SRMs) have being increased day by day, but this type of motors represents a highly nonlinear system, therefore there are a lot of difficulties in modeling and controlling them. We have proposed a non-linear mathematical model of a four phases 8/6 poles SRM then simulated it through Simulink/Matlab facilities. The whole control mechanism consists of a hysteresis current controller to minimize the torque ripple and a PID speed controller. The control design results are then validated in real-time by Simulink/Matlab software package.
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.
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.
This study uses intelligent techniques to regulate brushless direct current speed (BLDC) motors. After these motors solved the problem of using brushes and commutators in traditional DC motors, they succeeded in replacing brushes and commutators with electronic commutators. Due to the use of electronic switching, brushless motor algorithms are more complex than those of conventional motors. In this study, to adjust the PID controller's settings (Kp, Ki, and Kd), a trial-and-error approach was taken, and a completely new method known as the settings of known PID controllers have been modified using the new Gray Wolf algorithm. A BLDC motor's main benefit is that it has easy speed adjustment across a broad range, whereas AC motors often cannot be controlled in this way. Through the use of Matlab/Simulink, the BLDC motor's mathematical model was developed and implemented. The simulation results show that in the first case, a PID controller effectively induces the turbulent dynamic behavior of BLDC under load and no-load conditions, and in the second case, the speed shows the lowest rise time, stability, overshoot, and stability conditions, and performs at its best. The characteristics of the traditional PID controller that regulates the engine speed must be regulated online to achieve the use of intelligent technologies, and the adjustment is done online using the neural network. The results showed that this technology, or feature - online tuning - is the most effective and reliable of all.
In this paper, a design procedure which assumes general integer or noninteger order plant models ‘also can be unknown’ has been adopted to tune PID and fractional order PI (FOPI) controller. The design procedure depends on some specifications of frequency response of open loop system to ensure performance and robustness of step response of closed loop system. Firstly, the procedure is applied to integer order conventional PID (IOPID) controller, and then it has been extended to FOPI. Extensive simulation study has been made to investigate the performance of the obtained controllers, and also to compare between the two controllers. The simulation study has showed the validity and that the proposed controllers have good features in all of control demands, where it shows that these controllers have fast rise time with no overshoot and negligible steady state error. Also, it has showed that FOPI controller performs better than IOPID one.
This paper considers the neural network based PID controller. The learning and generalization properties of neural network are utilized in improving the performance of a conventional PID controller. Two different schemes are introduced. Both schemes are studied and their performances are comparatively evaluated on an example for uncertain system.
In this paper a combining Neurofuzzy and PID controllers have been employed for controlling the positions and rotational motions of the mini-helicopter system. Due to the strong coupling between the state variables of the mini-helicopter model, therefore, it is not suitable to design single controller for regulating the positions and rotational motions of the given model. To solve this problem, three neurofuzzy controllers are designed for the lateral, longitudinal and heave motion; and three classical PID controllers are proposed for attitude control. Nine rules are suggested for each neurofuzzy network depends on the previous knowledge/experiences of expert human pilot. The simulation results show that the proposed controllers are very effective to control the hovering, position and forward flight of the mini-helicopter system.
Over the past years, researchers have been focusing on development the robotics and actuation due to increase demand for these applications like industrial engineering, oil industry, healthcare, aerospace … etc. This work involves the design, construction and control of the Shape Memory Alloy (SMA) actuator. The industrial actuator has many characteristics able to be measured, which have an impact on the efficiency and effectiveness of the actuator while the execution of its tasks. The most important measurable characteristics are repeatability and accuracy. The current system typically is using Nitinol (Nickle Titanium Naval Ordinance Lab), which is one of the Shape Memory Alloy that contract when applying specific heat on it, and it can be used as an actuator. This work presents SMA in the shape of a spring to operate and control the accurate position of the 2-D system which containing four SMA springs, two SMA springs for the x -axis and two SMA springs for the y - axis. The theoretical design and calculations for SMA springs have been presented to collect information about the SMA springs. In a practical manner, the SMA spring characteristic like force and displacement were collected by a test bed that was designed and constructs before making the final rig. The setting shape of the SMA spring was presented and done as per the theoretical calculations. In the rig, each axis works as a two-direction actuator, the actuator is not prone to precise position points due to hysteresis and temperature variation. The SMA spring exhibited hysteresis and imprecise pointing, for that employing PID (Proportional Integral Derivative) with tracking mode controller to compensate the hysteresis. PID control system is played a decisive role with tracking mode model that achieves the aim behind the construction of the experimental rig. Good results have been obtained presented in three cases of drawing different shapes.
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.
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.
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.
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.)