Penerapan Model Predictive Control (MPC) pada Kapal Autopilot dengan Lintasan Tertentu

Permasalahan dalam kendali kapal salah satunya adalah path following. Path following bertujuan mengarahkan kapal untuk mengikuti jalur yang ditentukan. Pada penelitian ini dikaji bentuk pengendali untuk memperkuat kendali terhadap masalah path following. Kapal yang menjadi objek pada penelitian ini adalah kapal yang berjenis underactuated. Kapal underactuated merupakan kapal dengan jumlah variabel yang dikontrol lebih banyak dari jumlah aktuator, dimana aktuator adalah sebuah alat yang digunakan untuk mengontrol sebuah mekanisme atau sistem. Langkah pertama yang dilakukan adalah mentransformasikan model dinamik kapal terhadap lintasan sehingga didapatkan model dinamik kapal yang baru berupa error posisi kapal terhadap lintasan dan error orientasi (error sudut kapal). Selanjutnya digunakan pengendali MPC untuk menstabilkan gerak kapal.. Hasil simulasi menunjukkan bahwa dengan pengendali MPC, error dari path following konvergen ke nol dan kapal dapat mengikuti lintasan yang diharapkan

Aplikasi Model Predictive Control (MPC) Pada Optimasi Portofolio Komoditas

Komoditas merupakan salah satu jenis investasi yang disediakan oleh pasar modal kepada investor. Dalam investasi komoditas, terdapat dua hal yang menjadi pertimbangan investor, yaitu return dan risiko. Tujuan utama dalam investasi komoditas adalah memaksimalkan return dengan tingkat risiko tertentu atau meminimalkan risiko dengan tingkat return tertentu. Penentuan portofolio komoditas yang optimal merupakan salah satu hal yang sangat penting bagi kalangan investor. Pada penelitian ini digunakan metode pengendali Model Predictive Control (MPC) untuk menyelesaikan permasalahan optimasi portofolio komoditas dengan adanya kendala di dalam pembentukan portofolio. Data yang digunakan adalah data 3 komoditas yang diperdagangkan, yaitu emas, tembaga, dan minyak. Pengendali MPC dapat diterapkan dengan baik pada permasalahan optimasi portofolio komoditas. Dari hasil simulasi terlihat bahwa jumlah modal yang dimiliki investor yang merupakan output dari sistem menunjukkan peningkatan yang signifikan. Kenaikan ini terjadi karena jumlah modal yang diinvestasikan pada portofolio komoditas berusaha untuk mencapai reference trajectory yang ditetapkan. Selain itu state dan input dari sistem selalu berada di dalam batas constraint yang diberikan

Model Predictive Control (MPC) of quadrotors using LPV techniques

This Thesis objective was to apply Model predictive control (MPC) on a quadcopters using linear parameter varying (LPV) techniques. In order to do so the non-linear mathematical model of the quadcopter was put in the Linear Parameter Varying (LPV) form in order to be able to use the most basic Model Predictive Control (MPC) strategy, developed for linear systems. After applying the MPC strategy, the aim was to make the quadcopter track a given trajectory. Different trajectories were tested and validated. Initially, the most important step was to define the coordinate frames that are used to control the quadcopter and to establish the mathematical model of a quadcopter. Once the mathematical model of the quadcopter is developed, the next step was to design the controller. The controller was split into two sub controllers. One controller is responsible for the position variables x, y, z. This position controller controls the position variables by using the state feedback linearization method. Moreover, the attitude controller was used to control the angles using the LPV-MPC control strategy. The proposed strategy turned out to be a success in controlling the quadcopter. All the Quadcopter’s six degrees of freedom are tracked with very small errors. In tracking the x, y, z reference velocity values, one can observe strong overshoot at the beginning of the test period. That can be explained by the fact that the quadcopter starts its journey from quite a long distance away from the trajectory. However, once it reaches the path that it needs to follow, the velocities of the quadcopter stabilize and track the reference values very smoothly. Everything was done keeping in mind that the LPV-MPC controller needs time to push the state angles towards its reference values. Therefore, the attitude controller must work at a higher frequency compared to the position controlle

Linear Model Predictive Control of Induction Machine

This article presents new control algorithm for induction machine based on linear model predictive control (MPC). Controller works in similar manners as field oriented control (FOC), but control is performed in stator coordinates. This reduces computational demands as Park’s transformation is absent and induction machine mathematical model in stator coordinates contains less nonlinear elements. Another aim of proposed controller was to achieve fast torque response

Multi-objectives model predictive control of multivariable systems

In this thesis, MOO [Multi-Objective Optimization] design for Model Predictive Control (MPC) and Proportional Integral (PI) control are investigated for a multivariable process

Stability proof for nonlinear MPC design using monotonically increasing weighting profiles without terminal constraints

In this note, a new formulation of Model Predictive Control (MPC) framework with no stability-related terminal constraint is proposed and its stability is proved under mild standard assumptions. The novelty in the formulation lies in the use of time-varying monotonically increasing stage cost penalty. The main result is that the $0$-reachability prediction horizon can always be made stabilizing provided that the increasing rate of the penalty is made sufficiently high.Comment: Submitted to Automatic

Control of Inverse Response Processes by Model Predictive Control (MPC)

Due to the presence of Right Half Plane (RHP) zeros in the system, inverse response processes becomes hard to be identified and controlled. It happens when two separate effects are taking place at the same time but in different direction. Although inverse response problem is not infrequent to occur in industry especially chemical process industry, not many researchers pay attention towards controlling and solving this problem. In this paper, the author aims to compare the performance of the Model Predictive Control (MPC), Proportional Integral Derivative (PID), and Simple Internal Model Control (SIMC) in producing satisfactory control output for inverse response processes. Under this main objective, the author specifies it into three sub objectives. The first one is to design a MPC, PID, and SIMC for typical inverse response process. The second one is to measure the performance of the various controllers for set-point tracking or servo problem and lastly to compare the performance of the designed controllers. To ensure that all the objectives can be accomplished, proper methods need to be set and done throughout the progress of the project. To achieve objective 1, the author will make a proper selection on the type of controller to be used for this project and write the MATLAB coding for the selected controller, MPC, PID, and SIMC. It is crucial to identify the suitable methods and make a proper analysis on the performance of the controller. Three methods, Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time-weighted Absolute Error (ITAE) have been proposed in order to analyse and measure the performance of the designed controller. These methods will be further elaborated in the research methodology part of this paper. And in the end, to attain last objective, the author will compare the results of measurements based on set-point tracking condition. This project principally covers the simulation analysis and project design where the author will accomplish most of the task by using the MATLAB software during the course of this project. In end of this project, the author determines that MPC provides the quickest response compared to PID and SIMC controller other than producing satisfactory overall performance and is suitable to be used to control an inverse response process. Not only that this project can achieve its objective within time constraint, it is also feasible as the project is easily understood and the software that will be used to design the controller is fairly available