Application of Genetic Programming for Fine Tuning PID Controller Parameters Designed Through Ziegler-Nichols Technique

Position Tracking Performance with Fine Tune Ziegler-Nichols PID Controller for Electro-Hydraulic Actuator in Aerospace Vehicle Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/2107/1/012064 ◽

2021 ◽

Vol 2107 (1) ◽

pp. 012064

Author(s):

S.M. Othman ◽

Noorhazirah Sunar ◽

Hassrizal H.B ◽

A.H. Ismail ◽

M.N. Ayob ◽

...

Keyword(s):

Pid Controller ◽

External Disturbance ◽

Viscous Friction ◽

Fine Tuning ◽

Tracking Performance ◽

Position Tracking ◽

Trial And Error ◽

Hydraulic Actuator ◽

Tuning Method ◽

Non Linear System

Abstract Electro-Hydraulic Actuator (EHA) system is a third order non-linear system which is highly suffer from system uncertainties such as Coulomb friction, viscous friction and pump leakage coefficient which makes this system more complicated for the designing of the controller. The Proportional-Integral-Derivative (PID) controller has proposed in this paper to control EHA system and main problem in its application is to tune the parameter to its optimum value. Two different methods are used to tune the PID controller which are trial and error and Ziegler-Nichols method. MATLAB Simulink is used to simulate the system. In order to determine the performance of EHA system for the position tracking. 3 different of external disturbance such as 0N, 5000N and 10000N has been injected into the system. Simulation results show that the Ziegler-Nichols fine tuning method provides the better tracking performance when compared to the trial and error method for every specific disturbance setting. The Ziegler Nichols method provides better disturbance rejection as the performances indexes such as percentage overshoot, settling time and steady state error are not affected by the varying of disturbance.

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Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System

International Journal of Energy Optimization and Engineering ◽

10.4018/ijeoe.2016100104 ◽

2016 ◽

Vol 5 (4) ◽

pp. 62-83 ◽

Cited By ~ 1

Author(s):

Dipayan Guha ◽

Provas Kumar Roy ◽

Subrata Banerjee

Keyword(s):

Power System ◽

Pid Controller ◽

Frequency Control ◽

Thermal Power ◽

Load Frequency Control ◽

Fine Tuning ◽

Average Error ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Load Frequency

In this article, a novel optimization algorithm called grey wolf optimization (GWO) with the theory of quasi-oppositional based learning (Q-OBL) is proposed for the first time to solve load frequency control (LFC) problem. An equal two-area thermal power system equipped with classical PID-controller is considered for this study. The power system network is modeled with governor dead band and time delay nonlinearities to get better insight of LFC system. 1% load perturbation in area-1 is considered to appraise the dynamic behavior of concerned power system. Integral time absolute error and least average error based fitness functions are defined for fine tuning of PID-controller gains employing the proposed method. An extensive comparative analysis is performed to establish the superiority of proposed algorithm over other recently published algorithms. Finally, sensitivity analysis is performed to show the robustness of the designed controller with system uncertainties.

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Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Algorithms ◽

10.3390/a11070108 ◽

2018 ◽

Vol 11 (7) ◽

pp. 108 ◽

Cited By ~ 3

Author(s):

Natalia Alekseeva ◽

Ivan Tanev ◽

Katsunori Shimohara

Keyword(s):

Genetic Programming ◽

Autonomous Vehicles ◽

Pid Controller ◽

Control Model ◽

Servo Control ◽

Experimental Results ◽

Pd Controller ◽

Quality Of Control ◽

Computing Paradigm

The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

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Performance enhancement of split-phase induction motor by using fuzzy-based PID controller

Journal of Electrical Engineering ◽

10.2478/jee-2019-0016 ◽

2019 ◽

Vol 70 (2) ◽

pp. 103-112

Author(s):

Mohamed I. Abdelwanis ◽

Ragab A. El-Sehiemy

Keyword(s):

Steady State ◽

Induction Motor ◽

Pid Controller ◽

Pulse Width Modulation ◽

Performance Enhancement ◽

Fine Tuning ◽

Motor Speed ◽

Pumping System ◽

Total Oscillation ◽

State Error

Abstract This paper presents control and analysis of a split-phase induction motor (SPIM) to drive a centrifugal pumping system. An optimized proportional- integral and derivative (PID) controller, that is capable with a vector closed-loop split-phase induction motor control, is presented and its simulation results are discussed. The fine-tuning procedure is employed for fuzzy PID (FPID) controller parameters in order to sustain the motor speed at the predefined reference values. To assess the performance of the competitive controllers, conventional PID (CPID) and FPID, four operational indices for are suggested for measure the capability of the two controllers. These indices involve individual steady state error (ISSE) for each operating period, total steady state error (TSSE) for overall loading cycle, Individual oscillation index (IOI) and Total oscillation index (TOI), in order to measure the capability of the FPID compared with CPID. The performance of the SPIM accomplished with these performance indices is checked and tested on high and low speed levels. Pulse width modulation (PWM) based simulation studies were employed for SPIM using MATLAB/SIMULINK software. The results show that the overall performance of the SPIM operated with vector control that is tuned by FPID is enhanced compared with CPID.

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Identification and Control of Impressed Current Cathodic Protection System

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.12.2.12 ◽

2016 ◽

Vol 12 (2) ◽

pp. 214-220 ◽

Cited By ~ 1

Author(s):

Bassim Sada ◽

Ramzy Ali ◽

Khearia Ali

Keyword(s):

Cathodic Protection ◽

Pid Controller ◽

Fuzzy Controller ◽

Fine Tuning ◽

Control Technique ◽

Temperature Conductivity ◽

Impressed Current ◽

And Control ◽

Impressed Current Cathodic Protection ◽

Identification Model

In this paper the identification and control for the impressed current cathodic protection (ICCP) system are present. Firstly, an identification model using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was implemented. The identification model consists of four inputs which are the aeration flow rates, the temperature, conductivity, and protection current, and one output that represented by the structure-to-electrolyte potential. The used data taken from an experimental CP system model, type impressed current submerged sample pipe carbon steel. Secondly, two control techniques are used. The first control technique use a conventional Proportional-Integral-Derivative (PID) controller, while the second is the fuzzy controller. The PID controller can be applied to control ICCP system and quite easy to implement. But, it required very fine tuning of its parameters based on the desired value. Furthermore, it needed time response more than fuzzy controller to track reference voltage. So the fuzzy controller has a faster and better response.

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Fine tuning of a PID controller with inlet derivative filter using Pareto solution for gantry crane systems

Alexandria Engineering Journal ◽

10.1016/j.aej.2021.12.017 ◽

2021 ◽

Author(s):

Khaled T. Mohamed ◽

Mahmoud H. Abdel-razak ◽

Eman H. Haraz ◽

Atef A. Ata

Keyword(s):

Pid Controller ◽

Fine Tuning ◽

Gantry Crane ◽

Pareto Solution ◽

Derivative Filter

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Speed Control Optimization for Autonomous Vehicles with Metaheuristics

Electronics ◽

10.3390/electronics9040551 ◽

2020 ◽

Vol 9 (4) ◽

pp. 551 ◽

Cited By ~ 3

Author(s):

José Eugenio Naranjo ◽

Francisco Serradilla ◽

Fawzi Nashashibi

Keyword(s):

Autonomous Vehicles ◽

Pid Controller ◽

Error Function ◽

Absolute Error ◽

Optimization Methods ◽

Research Area ◽

Point Of View ◽

Fine Tuning ◽

Cruise Control ◽

Pid Tuning

The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes.

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