The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.
The main problem of line follower robot is how to make the mobile robot follows a desired path (which is a line drawn on the floor) smoothly and accurately in shortest time. In this paper, the design and implementation of a complex line follower mission is presented by using Matlab Simulink toolbox. The motion of mobile robot on the complex path is simulated by using the Robot Simulator which is programed in Matlab to design and test the performance of the proposed line follower algorithm and the designed PID controller. Due to the complexity of selection the parameters of PID controller, the Particle Swarm Optimization (PSO) algorithm are used to select and tune the parameters of designed PID controller. Five Infrared Ray (IR) sensors are used to collect the information about the location of mobile robot with respect to the desired path (black line). Depending on the collected information, the steering angle of the mobile robot will be controlled to maintain the robot on the desired path by controlling the speed of actuators (two DC motors). The obtained simulation results show that, the motion of mobile robot is still stable even the complex maneuver is performed. The hardware design of the robot system is perform by using the Arduino Mobile Robot (AMR). The Simulink Support Package for Arduino and control system toolbox are used to program the AMR. The practical results show that the performances of real mobile robot are exactly the same of the performances of simulated mobile robot.
PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.
The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time.
In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.
The objective of this paper is to design an efficient control scheme for car suspension system. The purpose of suspension system in vehicles is to get more comfortable riding and good handling with road vibrations. A nonlinear hydraulic actuator is connected to passive suspension system in parallel with damper. The Particles Swarm Optimization is used to tune a PID controller for active suspension system. The designed controller is applied for quarter car suspension system and result is compared with passive suspension system model and input road profile. Simulation results show good performance for the designed controller I. I NTRODUCTION Suspensions systems can be classified into three types are (passive, simi active and active). Figs. 1, 2 and 3 below shows the three types of Quarter car suspension system and hydraulic actuator position in each type.[1] Fig. 1 Passive Quarter Car Model Fig. 2 Simi-Active Quarter Car Model Fig. 3 Active Quarter Car Model In passive suspension systems the main parts are springs and hydraulic dumpers. The main job of these dumpers is to decrease the road profile and vibration effects into driver and passenger’s cabin. In active suspension system there are three parts under spring mass (body of car), spring, dumper and hydraulic actuator are connected in parallel. In this paper an additional parts is added to passive suspension system in parallel with springs and dumpers called a hydraulic actuator to get an active suspension system. This hydraulic actuator is a nonlinear part and it is controlled by spool valve. The mechanism of this actuator is to decrease the road profile and vibration from passive suspension system to get more comfortable riding. By using PID controller trained by Particle Swarm Optimization (PSO) to find optimal values of proportional, divertive and Quarter Car Active Suspension System Control Using PID Controller tuned by PSO Wissam H. Al-Mutar Turki Y. Abdalla Electrical Eng. Computer Eng. University of Basrah University of Basrah Basrah. Iraq. Basrah. Iraq. Spring Mass Unpring Mass K Kt C Ct Spring Mass Unpring K K C C Spring Mass Unpring Mass K Kt C F Ct اﻟﻤﺠﻠﺔ اﻟﻌﺮاﻗﻴﺔ ﻟﻠﻬﻨﺪﺳﺔ اﻟﻜﻬﺮﺑﺎﺋﻴﺔ واﻻﻟﻜﺘﺮوﻧﻴﺔ Iraq J. Electrical and Electronic Engineering ﻡﺠﻠﺪ 11 ، اﻟﻌﺪد 2 ، 2015 Vol.11 No.2 , 2015 Active suspension, PSO, PID controller, quarter car
This article emphasizes on a strategy to design a Super Twisting Sliding Mode Control (STSMC) method. The proposed controller depends on the device of Field Programmable Gate Array (FPGA) for controlling the trajectory of robot manipulator. The gains of the suggested controller are optimized using Chaotic Particle Swarm Optimization (PSO) in MATLAB toolbox software and Simulink environment. Since the control systems speed has an influence on their stability requirements and performance, (FPGA) device is taken in consideration. The proposed control method based on FPGA is implemented using Xilinx block sets in the Simulink. Integrated Software Environment (ISE 14.7) and System Generator are employed to create the file of Bitstream which can be downloaded in the device of FPGA. The results show that the designed controller based of on the FPGA by using System Generator is completely verified the effectiveness of controlling the path tracking of the manipulator and high speed. Simulation results explain that the percentage improvement in the Means Square Error (MSEs) of using the STSMC based FPGA and tuned via Chaotic PSO when compared with the same proposed controller tuned with classical PSO are 17.32 % and 13.98 % for two different cases of trajectories respectively.
In this paper, fuzzy Petri Net controller is used for Quadrotor system. The fuzzy Petrinet controller is arranged in the velocity PID form. The optimal values for the fuzzy Petri Net controller parameters have been achieved by using particle swarm optimization algorithm. In this paper, the reference trajectory is obtained from a reference model that can be designed to have the ideal required response of the Quadrotor, also using the quadrotor equations to find decoupling controller is first designed to reduce the effect of coupling between different inputs and outputs of quadrotor. The system performance has been measured by MATLAB. Simulation results showed that the FPN controller has a reasonable robustness against disturbances and good dynamic performance.
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.