Desain dan Simulasi Gerak Kontrol Kedalaman Pada MARES AUV Menggunakan Nonlinear Model Predictive Control

Autonomous Underwater Vehicles (AUV) merupakan suatu sistem yang nonlinier. Kesulitan masalah desain sistem kontrol pada underwater vehicles dikarenakan dinamika nonlinier-nya, model tak tentu, dan kemunculan disturbance yang susah untuk diukur atau diestimasi. Dinamika kontrol dari vehicle membutuhkan jaminan kestabilan dan tampil secara konsisten. Kesulitan masalah desain sistem kontrol pada dinamika AUV adalah metodologi desain tradisional linier tidak dapat diakomodasi secara mudah. Pada penelitian ini dibangun nonlinear disturbance observer yang didapatkan dari model predictive control law, digunakan untuk memprediksi melebihi horizon prediksi sehingga menghasilkan control signal sequences. Diharapkan output dapat mengikuti referensi yang diberikan juga melakukan noise cancelation dan online optimization. Dalam tesis ini, NMPC diterapkan langsung pada model nonlinier tanpa melakukan linierisasi terlebih dahulu untuk mengatasi masalah tracking control dalam pengaturan kedalaman pada MARES AUV. Hasil simulasi menunjukkan bahwa implementasi NMPC yang diusulkan dapat menggiring error kedalaman menuju 0 di waktu ke 1200 detik, sehingga hal ini membuktikan bahwa NMPC secara efektif dapat digunakan pada model nonlinear dengan multi input dan multi output. ============================================================================= Autonomous Underwater Vehicles (AUV) is a nonlinear system. Difficulties of control system design problems in underwater vehicles due to their nonlinear dynamics, indeterminate models, and the emergence of disturbances that are difficult to measure or estimate. The dynamics of control of the vehicle requires a guarantee of stability and consistent performance. The difficulty of control system design problem in AUV dynamics is the linear traditional design methodology can not be accommodated easily. In this thesis, a nonlinear disturbance observer build derived from predictive control law model, used to predict over prediction horizon to produce control signal sequences in order to follow the reference that given, noise cancelation, and online optimization, so NMPC applied directly to nonlinear model without doing linearization in advance to solve the problem of tracking control in depth control on the MARES AUV. The simulation results show that NMPC controllers herd the depth error to 0 at 1200 second so this approve that NMPC implementation can effectively be used on nonlinear models with multi input and multi output

Kontrol Kelembaban Pada Media Budidaya Cacing Lumbricus Terrestris Dengan Metoda Fuzzy

Lumbricus worm is one of the cultivated animals wich contain high protein. Other benefit of lumbricus worm is can be medicine for tipes. In worm cultivation there are parameters that we have to control. These parameter is moisture of media and air temperature around cultivation media. Based on that problem, this plant is designed to control moisture media of cultivation and air temperature with fuzzy metode. The component that use in this plant is soil moisture sensor, DHT11, Arduino, and pump dc for actuator. The mechanism of this plant in these sensor will read parameter and send the data to Arduino, the data will be processed with fuzzy metode, the output of fuzzy metode is timer that use to trigger the pump on and off. The result of final tes is the plant can control moisture of media cultivation and air temperature properly. The final weight of worm from harves is increase 40% from the seed dispersal. From that case can conclude that implementation of this plant is give real positif affect to harves of lumbricus terrestris wor

Fish Swarmed Kalman Filter for State Observer Feedback of Two-Wheeled Mobile Robot Stabilization

Over the past few decades, there have been significant technological advancements in the field of robots, particularly in the area of mobile robots. The performance standards of speed, accuracy, and stability have become key indicators of progress in robotic technology. Self-balancing robots are designed to maintain an upright position without toppling over. By continuously adjusting their center of mass, they can maintain stability even when disturbed by external forces. This research aims to achieving and maintaining balance is a complex task. Self-balancing robots must accurately sense their orientation, calculate corrective actions, and execute precise movements to stay upright. Eliminating disturbances and measurement noise in self-balancing robot can enhance the accuracy of their output. One common technique for achieving this is by using Kalman filters, which are effective in addressing non-stationary linear plants with unknown input signal strengths that can be optimized through filter poles and process covariances. Additionally, advanced Kalman filter methods have been developed to account for white measurement noise. In this research, state estimation was conducted using the Fish Swarm Optimization Algorithm (FSOA) to provide feedback to the controller to overcome the effects of disturbances and noise in the measurements through the designed filter. FSOA mimics the social interactions and coordinated movements observed in fish groups to solve optimization problems. FSOA is primarily used for optimization tasks where finding the global optimal solution is desired. The results show that the use of an optimized Kalman filter with FSOA on a two-wheeled mobile robot to handle system stability reduces noise values by 38.37%, and the system reaches a steady state value of 3.8 s with a steady error of 0.2%. In addition, by using the proposed method, filtering disturbances and measurement noise in self-balancing robot can help improve the accuracy of the self balancing robot’s output. System response becomes faster towards stability compared to other methods which are also applied to two-wheeled mobile robots

Monitoring Dan Pengendalian Continuous Flow Mixing Menggunakan SIMATIC PCS 7 Dengan Metode Model Predictive Control

Model Predictive Control adalah metode aljabar linear untuk memprediksi sinyal urutan manipulasi variabel kontrol. MPC adalah sarana yang digunakan secara luas untuk menangani masalah besar kontrol multivariabel yang disertai dengan kendala-kendala di industri. Alat Continuous Flow Mixing adalah alternatif alat yang sering digunakan di industri untuk mempercepat proses pengerjaan suatu plant mixing.Continuous Flow Mixing yang dirancang pada skripsi ini adalah alat pencampuran dua cairan secara kontinyu dimana proses dosing, mixing, dan drain terjadi secara serempak. Masalah krusial yang terjadi pada Continuous Flow Mixing adalah proses pengukuran atau yang disebut dengan dosing pada skripsi ini yang akan dicoba untuk  ditangani oleh kontroler Model Predictive Control. Penggunaan DCS tipe SIMATIC PCS 7 memudahkan dalam pembuatan program kontinyu maupun sekuensial untuk sistem yang kompleks dengan membuat program CFC dan SFC . Pengujian respon sistem dilakukan terhadap variasi setting point. Dari hasil pengujian didapatkan data-data yang diperoleh menunjukkan bahwa respon sistem cukup baik dalam mengejar nilai setting point dalam berbagai variasi nilai setting point. Kata Kunci:Model Predictive Control, SIMATIC PCS 7, Continuous Flow Mixin

PENERAPAN METODE HYSTERESIS SPACE VECTOR PULSE WIDTH MODULATION PADA INVERTER TIGA FASA UNTUK PENGATURAN KECEPATAN DAN EFISIENSI MOTOR INDUKSI

Pengaturan kecepatan motor induksi relatif sulit, karena torsi dan fluks yang dihasilkan saling berkaitan. Selain itu untuk mengatur kecepatan diperlukan inverter sebagai aktuator. Keluaran inverter bukan sinyal sinusoidal murni tetapi hasil dari pensaklaran. Oleh karena itu diperlukan metode pensaklaran untuk dapat memperbaiki sinyal keluaran inverter agar dapat meningkatkan efisiensi dan mengatur kecepatan motor induksi.  Pada penelitian ini metode indirect vector control diterapkan untuk pengaturan kecepatan dan menggabungkan metode SVPWM (Space vector pulse width modulation) dengan Hystesesis sehingga menjadi metode hysteresis space vector pulse width modulation (HSVPWM). Hasil simulasi pengaturan kecepatan motor induksi tiga fasa menggunakan metode indirect vector control berhasil diterapkan yaitu dapat mengikuti set point sebesar 600 rpm dengan rise time 0.527 detik, steady state 0.723 detik dengan overshoot sebesaar 0.8%. Ripple arus keluaran pada inverter menggunakan metode HSVPWM dapat berkurang 65%. Effisiensi motor induksi menggunakan metode HSVPWM yang semula 91% dapat ditingkatkan menjadi 94% atau kenaikan 3%. Kata Kunci -Indirect Vector Control, Space vector pulse width modulation, Hysteresis Band, Inverter, Motor Induks

Monitoring Dan Pengendalian Continuous Flow Mixing Menggunakan SIMATIC PCS 7 Dengan Metode Model Predictive Control

Model Predictive Control adalah metode aljabar linear untuk memprediksi sinyal urutan manipulasi variabel kontrol. MPC adalah sarana yang digunakan secara luas untuk menangani masalah besar kontrol multivariabel yang disertai dengan kendala-kendala di industri. Alat Continuous Flow Mixing adalah alternatif alat yang sering digunakan di industri untuk mempercepat proses pengerjaan suatu plant mixing.Continuous Flow Mixing yang dirancang pada skripsi ini adalah alat pencampuran dua cairan secara kontinyu dimana proses dosing, mixing, dan drain terjadi secara serempak. Masalah krusial yang terjadi pada Continuous Flow Mixing adalah proses pengukuran atau yang disebut dengan dosing pada skripsi ini yang akan dicoba untuk  ditangani oleh kontroler Model Predictive Control. Penggunaan DCS tipe SIMATIC PCS 7 memudahkan dalam pembuatan program kontinyu maupun sekuensial untuk sistem yang kompleks dengan membuat program CFC dan SFC . Pengujian respon sistem dilakukan terhadap variasi setting point. Dari hasil pengujian didapatkan data-data yang diperoleh menunjukkan bahwa respon sistem cukup baik dalam mengejar nilai setting point dalam berbagai variasi nilai setting point. Kata Kunci:Model Predictive Control, SIMATIC PCS 7, Continuous Flow Mixin

A Novel Design of Error Backpropagation Algorithm for Ingredient Mixing Process Tamarind Turmeric Herb

The goal of this study is to determine the best picture pattern for the tamarind turmeric herb. So far, the taste and color of tamarind turmeric herb have not been consistent, as they are impacted by maturity, and the amount of Turmeric used. The error backpropagation technique, which is commonly used in Content-based image retrieval systems, will be used to recognize image patterns. The main goal is to capture various portions of the tamarind turmeric herb during the extraction procedure. The camera is used to classify the tamarind turmeric herb product, process it into 5x5 pixels, and average the RGB value to obtain stable RGB values in each category, which are then fed into the Error Backpropagation algorithm. The most appropriate and fastest Error Backpropagation algorithm procedure will be found and implemented in a real-time computer. The first way will be to train the algorithm with ten data by changing neurons, layer, momentum, and learning rate, and the second technique will be to test the algorithm with ten data. The results of the training and testing procedure show that the two hidden layers can recognize 100% of inputs, with three input layers for R, G, and B values, ten neurons in the first and second hidden layers, and one output layer with Learning rate 0.5 and Momentum 0.6 as a parameter. Dark yellow is the best image pattern standard for tamarind turmeric herb, with RGB values in the range from 255, 103, 32 to 255, 128, 48