OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM

Servomotor uses feedback controller to control the speed or the position, or both. Typically, the PID controller is used and has evolved into more recent approaches like the hybrid with fuzzy logic controller (FLC) or neural network (NN). Many tuning methods for PID controller have been developed, and one of them is based on natural evolution, the genetic algorithm (GA). The significant drawback of GA is that the optimization process needs too many iterations and too long duration. In this thesis, a new optimization GA-based algorithm that emanates from modification of conventional GA to reduce the iterations number and the duration time, namely, semi-parallel operation genetic algorithm (SPOGA) is proposed. The aim of the algorithm is to improve a controller performance (minimize the overshoot, settling time, IAE/ITAE and achieving zero steady state error) when used for a DC servomotor application. This controller would be optimized to obtain the best overall performances of the performance criterion. The servomotor's transfer function is obtained via system identification and is modelled using MATLAB commands. The model is used in the simulation of speed and position control and the performance of relevant conventional, fuzzy, and hybrid controllers are compared for various predefined conditions. The best controller is then selected to be optimized using SPOGA. Next, the performance comparison of GA and SPOGA is conducted based on the maximum value of parallel functions obtained. The SPOGA is then used to optimize the selected controllers and the performance comparisons of the controllers were conducted. Detailed performance comparisons of controllers for a DC servomotor speed and position control under seven predefined conditions is presented. As compared to conventional GA, SPOGA performs better in reducing the number of test runs with the same results. The findings demonstrate the effectiveness of the hybrid-fuzzy controller for speed and position control of a DC servomotor, and confirm the ability of SPOGA as an optimization algorithm for the hybrid-fuzzy controller

OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM

Servomotor uses feedback controller to control the speed or the position, or both. Typically, the PID controller is used and has evolved into more recent approaches like the hybrid with fuzzy logic controller (FLC) or neural network (NN). Many tuning methods for PID controller have been developed, and one of them is based on natural evolution, the genetic algorithm (GA). The significant drawback of GA is that the optimization process needs too many iterations and too long duration. In this thesis, a new optimization GA-based algorithm that emanates from modification of conventional GA to reduce the iterations number and the duration time, namely, semi-parallel operation genetic algorithm (SPOGA) is proposed. The aim of the algorithm is to improve a controller performance (minimize the overshoot, settling time, IAE/ITAE and achieving zero steady state error) when used for a DC servomotor application. This controller would be optimized to obtain the best overall performances of the performance criterion. The servomotor's transfer function is obtained via system identification and is modelled using MATLAB commands. The model is used in the simulation of speed and position control and the performance of relevant conventional, fuzzy, and hybrid controllers are compared for various predefined conditions. The best controller is then selected to be optimized using SPOGA. Next, the performance comparison of GA and SPOGA is conducted based on the maximum value of parallel functions obtained. The SPOGA is then used to optimize the selected controllers and the performance comparisons of the controllers were conducted. Detailed performance comparisons of controllers for a DC servomotor speed and position control under seven predefined conditions is presented. As compared to conventional GA, SPOGA performs better in reducing the number of test runs with the same results. The findings demonstrate the effectiveness of the hybrid-fuzzy controller for speed and position control of a DC servomotor, and confirm the ability of SPOGA as an optimization algorithm for the hybrid-fuzzy controller

Aplikasi logika Kabur Untuk Mengendalikan Kecepatan Motor DC Menggunakan Penggendalian Logika Terpogram

This research tries to develop a fuzzy control software for the speed control of loaded dc motor using programmable logic controller. The first step is to get the output characteristics of dc motor. The next step is to control the speed of dc motor using Ziegler Nichols tuned proportional controller. Based on the motor characteristics, the membership functions and rules of fuzzy logic controller can be established, followed by computer programming The perfomance of the fuzzy logic controller is then compared with the perfomance of proportional controller. If we look at the transient characteristics, the system controlled by fuzzy logic controller has shorter raise time than that one controlled by proportional controller. From the experiment, the system controlled by fuzzy logic controller has no overshoot and oscillation for some variations of setpoint. The fuzzy logic controller is just also tuned once for some variations of setpoint. A bit dculty of this research is, the PLC does not recognize real numbers. Consequently, a number is represented by a thousand number, and its accuracy is limited

Kalman Filter to Improve Performance of PID Control Systems on DC Motors

A proportional–integral–derivative (PID) controller is a type of control system that is most widely applied in industrial world. Various tuning models have been developed to obtain optimal performance in PID control. However, the methods are designed under ideal circumstances. This means that the control system which has been built will not work optimally when noise exists. Noise can come from electrical vibrations, inference of electronic components, or other noise sources. Thus, it is necessary to design PID control system that can work optimally without being disturbed by noise. In this research, Kalman filter was used to improve the performance of PID controllers. The application of Kalman filter was used to reduce the noise of the input signal so that it could generate output signal which is in accordance with the expected output. Simulation result showed that the PID performance with Kalman filter was more optimal than the ordinary one to minimize the existing noise. The resulting speed of DC motor with Kalman filter had a lower overshoot than PID control without Kalman filter. This method resulted lower integral of absolute error (IAE) than ordinary PID controls. The IAE value for the PID controller with the Kalman filter was 25.4, the PID controller with the observer was 31.0, while the IAE value in the ordinary controller was only 60.9

Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

The purpose of this paper is to present an algorithm to determine the shortest path for quadrotor to be able to navigate in an unknown area. The problem in robot navigation is that a robot has incapability of finding the shortest path while moving to the goal position and avoiding obstacles. Hence, a modification of several algorithms are proposed to enable the robot to reach the goal position through the shortest path. The algorithms used are fuzzy logic and cell decomposition algorithms, in which the fuzzy algorithm which is an artificial intelligence algorithm is used for robot path planning and cell decomposition algorithm is used to create a map for the robot path, but the merger of these two algorithms is still incapable of finding the shortest distance. Therefore, this paper describes a modification of the both algorithms by adding potential field algorithm that is used to provide weight values on the map in order for the quadrotor to move to its goal position and find the shortest path. The modification of the algorithms have shown that quadrotor is able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is more rapid

DC Motor Speed Control Using Hybrid PID-Fuzzy with ITAE Polynomial Initiation

DC motors are widely applied in industrial sector, especiallyprocesses of automation and robotics. Given its role in the sector, DC motor operation needs to be optimized. One of optimization steps is controlling speed using several control methods, for example conventional PID methods, PID Ziegler Nichols, PID based on ITAE polynomials, and Hybrid PID-Fuzzy. From these methods, Hybrid PID-Fuzzy was chosen as a method to be proposed in this paper because it can anticipate shortcomings of PID controllers and fuzzy controllers so as to produce system responses that are fast and adaptive to errors. This paper aimed to design a Hybrid PID-Fuzzy system based on ITAE polynomials (Hybrid-ITAE), to analyze its performance parameters values, and tp compare Hybrid-ITAE performance with conventional PID method. Working parameters being reviewed include overshoot, rise time, settling time, and ITAE. First of all, JGA25-370 DC motor was modeled in a form of a third order transfer function equation. Based on the transfer function, PID parameters were calculated using PID Output Feedback and ITAE polynomial methods. The best ITAE polynomial PID controllers were then be combined with a Fuzzy Logic Controller to form a Hybrid-ITAE system. Simulation and experimental stages were carried out in two conditions, namely no load and loaded. Simulation and experimental results showed that Hybrid-ITAE (l = 0.85) was the best controller for no-load simulation conditions. For loaded simulation Hybrid-ITAE (l=1) was a better controller. In no-loads experiment, the best controller was Hybrid PID-Ziegler Nichols, while for loaded condition the best controller was Hybrid PID-Ziegler Nichols

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $0.7415$ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %

Path Planning Based on Fuzzy Decision Trees and Potential Field

The fuzzy logic algorithm is an artificial intelligence algorithm that uses mathematical logic to solve to by the data value inputs which are not precise in order to reach an accurate conclusion. In this work, Fuzzy decision tree (FDT) has been designed to solve the path planning problem by considering all available information and make the most appropriate decision given by the inputs. The FDT is often used to make a path planning decision in graph theory. It has been applied in the previous researches in the field of robotics, but it still shows drawbacks in that the robot will stop at the local minima and is not able to find the shortest path. Hence, this paper combines the FDT algorithm with the potential field algorithm. The potential field algorithm provides weight to the FDT algorithm which enables the robot to successfully avoid the local minima and find the shortest path