scholarly journals OPTIMIZATION OF PID PARAMETERS BASED ON PARTICLE SWARM OPTIMIZATION FOR BALL AND BEAM SYSTEM

2020 ◽  
Vol 5 (9) ◽  
pp. 59-69
Author(s):  
Haytem Ali ◽  
Abdulgani Albagul ◽  
Alhade Algitta
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Trial And Error ◽  
Swarm Optimization ◽  
Beam Control ◽  
Trial And Error Method ◽  
Error Method

This paper introduces the application of an optimization technique, known as Particle Swarm Optimization (PSO) algorithm to the problem of tuning the Proportional-Integral-Derivative (PID) controller for a linearized ball and beam control system. After describing the basic principles of the Particle Swarm Optimization, the proposed method concentrates on finding the optimal solution of PID controller in the cascade control loop of the Ball and Beam Control System. Ball and Beam control system tends to balance a ball on a particular position on the beam as defined by the user. The efficiency of Particle Swarm Optimization algorithm for tuning the controller will be compared with a classical method, Trial and Error method. The comparison is based on the time response performance. The two tuning methods have been developed by simulation study using Matlab\ m-file software. The evaluations show that Evolutionary method Particle Swarm Optimization (PSO) algorithm gives a much better response than trial and error method.

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

scholarly journals Analisis Penerapan Kendali Otomatis berbasis PID terhadap pH Larutan

2020 ◽  
Vol 8 (1) ◽  
pp. 163
Author(s):  
FEBRIAN HADIATNA ◽  
ALFI DZULFAHMI ◽  
DECY NATALIANA
Keyword(s):  
Control System ◽  
Pid Controller ◽  
Ph Value ◽  
Pi Controller ◽  
Test Results ◽  
Control Test ◽  
Trial And Error ◽  
Trial And Error Method ◽  
Nutrient Solutions ◽  
Error Method

ABSTRAK Nilai pH pada larutan nutrisi tanaman hidroponik merupakan salah satu parameter penting agar tumbuhan berkembang dengan baik. Salah satu metode pengendali otomatis adalah menggunakan pengendali PID. Penelitian ini bertujuan untuk menganalisis penerapan sistem pengendali pH larutan nutrisi menggunakan PID. Proses pengendalian diatur oleh larutan pH up dan larutan pH down. Pengujian pengendali PID terdiri dari pengujian pengendali Propotional (P), pengendali Propotional-Inrtegral (PI), dan Propotional-Integral-Derivative (PID). Hasil pengujian dengan menggunakan metode tunning trial and error diperoleh hasil pengendali Propotional menghasilkan respon lebih baik dengan nilai Kp=1, hasil yang didapat sistem tidak mengalami overshoot/undershoot dengan waktu yang dibutuhkan mencapai titik kesetimbangan di setpoint yaitu 126 detik. Agar sistem terjaga dinilai setpointnya sistem pengendali akan bereaksi terhadap perubahan error, sehingga memerlukan lebih banyak larutan pH up dan pH down. Kata kunci : Hidroponik, pH, Arduino, kendali PID. ABSTRACT The pH value of hydroponic-plants nutrient solution is one of the important parameters for plants to grow up well. This study aims to analyze the application of a system to control the pH of nutrient solutions using PID which is controlled by a pH up solution and a pH down solution. PID controller testing consists of Propotional (P) control test, Propotional-Inrtegral (PI) controller, and Propotional-Integral-Derivative (PID) controller. The test results obtained using the tunning trial and error method obtained a Propotional controller result have a better response with the value Kp=1, the system does not overshoot / undershoot. the time needed to reach a stable value at setpoint is 126 seconds. so that the system is maintained at its set point the control system will react to error changes, that is why it requires more pH up liquid and the pH liquid down. Keywords : Hydroponic, pH, Arduino, PID controller.

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

scholarly journals Performance of PID Controller Tuning based on Particle Swarm Optimization and Firefly Algorithm

2019 ◽  
Vol 8 (3S2) ◽  
pp. 225-230 ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Transient Responses ◽  
Swarm Optimization ◽  
Pid Controller Tuning ◽  
Tank System

This paper shows the study of tuning the Proportional-Integral-Derivatives (PID) in the application of coupled tank system. The controller was tuned by using an optimization technique which is a Firefly Algorithm (FA) and a Particle Swarm Optimization (PSO) Algorithm. Both FA and PSO performance were evaluated by using performance index of Integral Time Square Error (ITSE). The systems response of FA and PSO were gathered and compared in term of transient responses, ITSE and standard deviation by considering the system condition of with and without a disturbance. The simulation is conducted by using MATLAB software. The result shows that the FA giving a better system performance compared to PSO in term of overall transient responses.

scholarly journals PID Controller Design for Cruise Control System using Particle Swarm Optimization

2017 ◽  
Vol 43 (2) ◽  
pp. 30-35
Author(s):  
Ahmed Abdulnabi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Controller Design ◽  
Robustness Analysis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Peak Time ◽  
Cruise Control ◽  
Swarm Optimization ◽  
Best Response

This paper presents a design of a Proportional-Integral-Derivative (PID) controller for automobile cruise controlsystem. The parameters of the PID controller, which are the proportional ( ), derivative ( ) , and integrator ( ), have beenselected using Particle Swarm Optimization (PSO) algorithm. In this study, the overall system performance has beencompared with other predesigned controllers (conventional PID, Fuzzy logic PID, state space, and Genetic algorithm basedPID controller). The simulation result illustrates that PSO based PID controller gives the best response in terms of settlingtime, rise time, peak time, and maximum overshot. The robustness analysis shows that the system is robust despite thedeviations in some of the system parameters.

scholarly journals Reaction Control System Optimization for Maneuverable Reentry Vehicles Based on Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hang Gui ◽  
Ruisheng Sun ◽  
Wei Chen ◽  
Bin Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Optimization ◽  
Suboptimal Control ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Reentry Vehicles ◽  
Reaction Control System

This paper presents a new parametric optimization design to solve a class of reaction control system (RCS) problem with discrete switching state, flexible working time, and finite-energy control for maneuverable reentry vehicles. Based on basic particle swarm optimization (PSO) method, an exponentially decreasing inertia weight function is introduced to improve convergence performance of the PSO algorithm. Considering the PSO algorithm spends long calculation time, a suboptimal control and guidance scheme is developed for online practical design. By tuning the control parameters, we try to acquire efficacy as close as possible to that of the PSO-based solution which provides a reference. Finally, comparative simulations are conducted to verify the proposed optimization approach. The results indicate that the proposed optimization and control algorithm has good performance for such RCS of maneuverable reentry vehicles.

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