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

Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods