Fuzzy-PID Controller for Azimuth Position Control of Deep Space Antenna

The Deep Space Antennas are essential in achieving communication over very large distances. However, the pointing accuracy of this antenna needs to be as precise as possible to enable effective communication with the satellite. Therefore, this work addressed the pointing accuracy for a Deep Space Antenna using Fuzzy-PID control technique by improving the performance objectives (settling time, percentage overshoot rise time and mainly steady-state error) of the system. In this work, the PID controller for the system was first of all designed and simulated after which, a fuzzy controller was also designed and simulated using MATLAB and Simulink respectively for the sake of comparison with the fuzzy-PID controller. Then, the fuzzy-PID controller for the system was also designed and simulated using MATLAB and Simulink and it gives a better performance objective (rise time of 1.0057s, settling time of 1.6019s, percentage overshoot of 1.8013, and steady-state error of 2.195e-6) over the PID and fuzzy controllers respectively. Therefore, the steady state error shows improved pointing accuracy of 2.195e-6

Rancang Bangun Farming Box Dengan Pengaturan Suhu Menggunakan Fuzzy Logic Controller

Implementation of control systems has been carried out in many fields of science. One of it applications is in the agriculture fields. In this research we implemented a control system on farming in a box. Farming in a box is a system that uses old shipping containers for the purpose of growing plants in any environment. Inside shipping containers is fully assembled hydroponic pipe with air temperature control. In this research was built a little farming box from acryclic to imitate a shipping container. Main focus of this research is design an air temperature control using fuzzy logic controller. Fuzzy logic controller was choosen because many existing farming box use on off controller. In some application, fuzzy logic controller has better performance than on off controller. Farming box temperature is controlled by blowing cool air using an electric fan. In this case, cool air is produced by cold side of peltier. Electric fan speed is controlled by pulse width modulation signal (PWM) that generated from microcontroller. Air temperature data feedback is obtained from DHT 11 sensor that installed in a acrylic box. Sensor is physically connected with microcontroller and Fuzzy logic controller is embedded in microcontroller as an algorithm. Fuzzy logic controller was design with error temperature and error difference as an input, and duty cycle of PWM signal as output. Fuzzy logic controller system performs to reduce the temperature from 31,6 ° C to set poin 28° C in 71 seconds. Steady state error obtained by 1.28% and better than uncontrolled system that obtain steady state error 7,14%

Fuzzy Logic Speed Regulator for D.C. Motor Tuning

A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model.   This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors

A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.Comment: 14 page

Penerapan Kendali Cerdas Pada Sistem Tangki Air Menggunakan Logika Fuzzy

Implementasi kendali cerdas pada sistem tangki air menggunakan logic fuzzy disajikan pada makalah ini. Sistem tangki air yang merupakan sistem yang dikontrol adalah suatu model dari proses kontrol dengan sensor tunggal dan aktuator tunggal. Kendali logik Fuzzy sebagai kendali cerdas pada penelitian ini didisain dan diimplementasikan untuk membuat ketinggian air mengikuti Perubahan ketinggian air acuan secepat mungkin dan mempertahankan ketinggian air sedekat mungkin dengan ketinggian air acuan, dibawah variasi lingkungan. Proses disain dari kontrol logik fuzzy dilakukan menggunakan nilai error (e) dan beda error (de) ketinggian air diukur oleh sensor sedangkan keluaran kendali adalah input tegangan untuk mensupply motor pompa (u). Secara matematik, operasi fuzzy set dan aturan fuzzy diberlakukan pada input dan ouput ini untuk meminimalisasi harga error dan Perubahan error. Dari hasil eksperimen, kendali logik fuzzy mempunyai 7 set fuzzy untuk input error, 3 fuzzy set untuk Perubahan error dan 21 aturan fuzzy untuk aksi kendali. Eror “steady state” yang dihasilkan lebih kecil 37.5% dari pengendali konvensional PI/Proporsional dan Integral (sebagai pengendali pembanding). Untuk respon dari variasi ketinggian air, kendali logik fuzzy cukup cepat tetapi lebih lambat 55.5% dari pengendali PI.The implementation of intelligent controller on water tank system using fuzzy logic was discussed in this paper. Water tank system, which was controlled system in this research, was a model of process control with single sensor and single actuator (Single Input Single Output). Fuzzy logic controller as intelligent controller in this research were designed and implemented for making water level follow the reference water level change as fast as possible and keeping water level close to the reference water level under variation of environment. The design of fuzzy logic controller was conducted by using input value of error (e) and difference of error (de) water level were measured by sensor and the output of controller was input voltage to supply pump motor (u). Mathematically, fuzzy set operation and fuzzy rules were conducted to this input and ouput to minimize value of error and difference of error. From experiment results, fuzzy logic controller has 7 fuzzy set for error input, 3 fuzzy set for change of error and 21 fuzzy rules for control action. Steady state error was 37.5% smaller than PI/Proporsional and IntegraI controller (as reference controller). For respon of water level variation, fuzzy logic controller was fast enough, but 55.5% slower than PI controller

Preliminary design study of a fuzzy tracking controller applied to a crane type manipulator

[Abstract] The main purpose of a control system is usually to force the output to follow a reference input with zero steady state error while satisfying certain transient requirements such the settling time for rapid following, overshoot and smoothness of the transient response. This paper is concerned with finding a fuzzy rule-based controller achieved by learning from a virtual feedback (PID) controller capable for satisfying rapid following, zero steady state error and overshoot suppression applied to robotized manipulators dedicated to heavy loads or big container handlin

Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Fuzzy logic is a logic that has a degree of membership in the vulnerable 0 to 1. Fuzzy logic is used to translate a quantity that is expressed using language. Fuzzy logic is used as a control system because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this paper is to present a fuzzy control system implemented in a rocket tracking control system. The fuzzy control system is used to keep the rocket on track and traveling at a certain speed. The signal from the fuzzy logic control system is used to control the rocket thrust. The fuzzy Logic System was chosen as the controller because it is able to work well on non-linear systems and offers convenience in program design. Fuzzy logic systems have a weakness when working on systems that require very fast control such as rockets. With this problem, fuzzy logic is modified by adding second-order integral control to the modified fuzzy logic. The proposed algorithm shows that the missile can slide according to the ramp path at 12 m altitude of 12.78 at 12 seconds with a steady-state error of 0.78 under FLC control, at 10 m altitude of 10.68 at 10 seconds with a steady-state error of 0.68 with control integral FCL, at a height of 4 m is 4.689 at 4 seconds with a steady-state error of 0.689 with a second-order integral control of FCL. The missile can also slide according to the parabolic path with the second-order integral control of FCL at an altitude of 15.47 in the 4th minute with a steady-state error of 0

African vulture optimizer algorithm based vector control induction motor drive system

This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response

Inter-row Robot Navigation using 1D Ranging Sensors

In this paper a fuzzy logic navigation controller for an inter-row agricultural robot is developed and evaluated in laboratory settings. The controller receives input from one-dimensional (1D) ranging sensors on the robotic platform, and operated on ten fuzzy rules for basic row-following behavior. The control system was implemented on basic hardware for proof of concept and operated on a commonly available microcontroller development platform and open source software libraries. The robot platform used for experimentation was a small tracked vehicle with differential steering control. Fuzzy inferencing and defuzzification, step response and cross track error were obtained from the test conducted to characterize the transient and steady state response of the controller. Controller settling times were within 4 seconds. Steady state centering errors for smooth barrier navigation were found to be within 3.5% of center for 61 cm wide solid barrier tests, and within 38% for simulated 61 cm corn row tests

Inter-row Robot Navigation using 1D Ranging Sensors

In this paper a fuzzy logic navigation controller for an inter-row agricultural robot is developed and evaluated in laboratory settings. The controller receives input from one-dimensional (1D) ranging sensors on the robotic platform, and operated on ten fuzzy rules for basic row-following behavior. The control system was implemented on basic hardware for proof of concept and operated on a commonly available microcontroller development platform and open source software libraries. The robot platform used for experimentation was a small tracked vehicle with differential steering control. Fuzzy inferencing and defuzzification, step response and cross track error were obtained from the test conducted to characterize the transient and steady state response of the controller. Controller settling times were within 4 seconds. Steady state centering errors for smooth barrier navigation were found to be within 3.5% of center for 61 cm wide solid barrier tests, and within 38% for simulated 61 cm corn row tests