Distributed Estimation with Decentralized Control for Quadruple-Tank Process

This paper proposes the design of quadruple-tank process due to the unique multivariable MIMO system under minimum and non-minimum scenario with respect to the valve ratio. This model is then implemented the distributed estimation algorithm with decentralized control. The inputs are set in divergent gains of pumps while the four-tank process is interconnected so that the stability properties are different, making the usage of decentralized control is reasonable. The number of outputs is designed the same as those of inputs which are also that of distributed Luenberger observer with the continuous linearized dynamical system. This distributed comprises local estimates only in certain output, meaning that it would lead to insufficiency so that the neighbouring links under some network topologies are required in the dynamical system. This concept fortunately works in two different characteristic stability of the tank process regarding estimating the states. This success leads to the further research of the more large-scale complex system.Comment: 7 pages, 9 figure

Adaptive Kalman Filtering with Exact Linearization and Decoupling Control on Three-Tank Process

Water treatment and liquid storage are the two plants implementing the hydraulic three-tank system. Maintaining certain levels is the critical scenario so that the systems run as desired. To deal with, the optimal linear control and the complex advanced non-linear problem have been proposed to track certain dynamic reference. This paper studies those two using the combination of linearization and decoupling control under some assumptions. The result shows that the designed methods have successfully traced the dynamic reference signals. Beyond that, the adaptive system noise Kalman filter (AKF) algorithm is used to examine the estimation performance of the true non-linear system and the performance yields a rewarding prediction of the true system.Comment: 8 pages, 11 figure

Implementasi Metode Optimasi Particle Swarm Optimization (PSO) Untuk Tuning Pengendali Model Predictive Control (MPC) Pada Quadruple Tank

Pada penelitian ini telah dibangun mode kontrol Model Predictive Control (MPC) dengan metode optimasi Particle Swarm Optimization untuk mencari nilai terbaik pada parameter beban sinyal kontrol Wu dan sinyal control error W∆u yang kemudian diimplementasikan secara online pada rancang bangun system Quadruple Tank. Metode IMOPSO untuk MPC dengan nilai sinyal control Wu =0.0076 dan sinyal control error Wdu = 0.1221 menghasilkan respon system terbaik dengan maximum overshoot = 4% error steady state 1% settling time 55 detik dibandingkan MOPSO dengan nilai sinyal control Wu 0.0397 dan sinyal control error Wdu 0.1780 menghasilkan respon sistem dengan maksimum overshoot = 5% Error Steady State = 3 % settling time 65 detik. Selain itu, dibangun juga control PSO – PID yang digunakan sebagai pembanding dimana mode MOPSO menghasilkan nilai Kp = 3.0828 Ki = 0.4219 memiliki respon sistem dengan maksimum overshoot = 3 % Error Steady State = 2% dan settling time 250 detik. Sedangkan pada mode IMOPS nilai Kp = 2.9388 Ki = 0.2166 memiliki respon system dengan maksimum overshoot = 3 % Error Steady State 1.5% dan settling time 150 detik

Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.Comment: 13 pages, 33 figure

Implementasi Metode Optimasi Particle Swarm Optimization (PSO) untuk Tuning Pengendali Model Predictive Control (MPC) pada Quadruple Tank

Pada penelitian ini telah dibangun mode kontrol Model Predictive Control (MPC) dengan metode optimasi Particle Swarm Optimization untuk mencari nilai terbaik pada parameter beban sinyal kontrol Wu dan sinyal control error W∆u yang kemudian diimplementasikan secara online pada rancang bangun system Quadruple Tank. Metode IMOPSO untuk MPC dengan nilai sinyal control Wu =0.0076 dan sinyal control error Wdu = 0.1221 menghasilkan respon system terbaik dengan maximum overshoot = 4% error steady state 1% settling time 55 detik dibandingkan MOPSO dengan nilai sinyal control Wu 0.0397 dan sinyal control error Wdu 0.1780 menghasilkan respon sistem dengan maksimum overshoot = 5% Error Steady State = 3 % settling time 65 detik. Selain itu, dibangun juga control PSO – PID yang digunakan sebagai pembanding dimana mode MOPSO menghasilkan nilai Kp = 3.0828 Ki = 0.4219 memiliki respon sistem dengan maksimum overshoot = 3 % Error Steady State = 2% dan settling time 250 detik. Sedangkan pada mode IMOPS nilai Kp = 2.9388 Ki = 0.2166 memiliki respon system dengan maksimum overshoot = 3 % Error Steady State 1.5% dan settling time 150 detik

Overview of ground-based generator towers as cloud seeding facilities to optimize water resources in the Larona Basin

The Larona River Basin which cover an area of 2477 km2, including the three cascading lakes: Matano, Mahalona, and Towuti Lakes, is a strategic watershed which acts as the water resource for three hydropower plants that supply 420 Megawatt of electricity to power a nickel processing plant and its supporting facilities and electricity need of the surrounding communities. The maximum and minimum operating levels of Towuti Lake are 319.6 meters (asl) and 317.45 meters (asl) respectively. Total live storage between these two elevations is 1,231,500 m3. Currently, the operation average outflow from Towuti Lake to the power plants is 130.1 m3/second which is resulting in a total annual outflow volume of 4,103,000 m3. By comparing the outflow volume with the live storage volume, it is obvious that present live storage has a limited capability to carry over the capacity from wet to dry years. During a dry year, the outflow drops to 100 m3/second. Thus, the optimization of water resources management in the Larona Basin is important to fulfil the need to produce the energy sources. To deal with the decrease of the Lakes water level, the Weather Modification Technology in the form of cloud seeding is needed to produce rain that will increase the water volume in the Lakes. The dispersion of cloud seeding material into the targeted clouds can be done by surface seeding using the Ground-Based Generator (GBG) which utilize towers to release cloud seeding materials. The tower locations should be in certain altitude or higher locations and amounts in order to operate effectively with optimum results. The water discharges generated from the process is expected in accordance with the planning. The weather modification process is inefficient when the discharge is overflow the spillway channel. Cost incurred is in approximate of US$$11,133,258.36 if the company is utilizing Diesel Power Plant and Steam Power Plant instead of the weather modification technology

Implementasi Metode Optimasi Particle Swarm Optimization (PSO) untuk Tuning Pengendali Model Predictive Control (MPC) pada Quadruple Tank

Pada penelitian ini telah dibangun mode kontrol Model Predictive Control (MPC) dengan metode optimasi Particle Swarm Optimization untuk mencari nilai terbaik pada parameter beban sinyal kontrol Wu dan sinyal control error W∆u yang kemudian diimplementasikan secara online pada rancang bangun system Quadruple Tank. Metode IMOPSO untuk MPC dengan nilai sinyal control Wu =0.0076 dan sinyal control error Wdu = 0.1221 menghasilkan respon system terbaik dengan maximum overshoot = 4% error steady state 1% settling time 55 detik dibandingkan MOPSO dengan nilai sinyal control Wu 0.0397 dan sinyal control error Wdu 0.1780 menghasilkan respon sistem dengan maksimum overshoot = 5% Error Steady State = 3 % settling time 65 detik. Selain itu, dibangun juga control PSO – PID yang digunakan sebagai pembanding dimana mode MOPSO menghasilkan nilai Kp = 3.0828 Ki = 0.4219 memiliki respon sistem dengan maksimum overshoot = 3 % Error Steady State = 2% dan settling time 250 detik. Sedangkan pada mode IMOPS nilai Kp = 2.9388 Ki = 0.2166 memiliki respon system dengan maksimum overshoot = 3 % Error Steady State 1.5% dan settling time 150 detik

Predicting Liquid-Vapor (LV) composition at distillation column

This paper will present the development of nonlinear model of distillation column using neural networks approach. The model is accomplished in Nonlinear Auto Regressive with exogenous input (NARX) structure.This distillation column has two input and two output variables. The input variables are heat duty on the reboiler (Qr), and reflux flowrate (L), while the output variables are mole fraction of distillate (Xd) and molefraction bottm product (Xb). The training as well as validation data were generated using Amplitude Pseudo Random Binary Signal (APRBS) as excitation signal. The structure of neaural networks is feedforwardnetworks, which consists of three layers: input, hidden and output layer. Levenberg-Marquardt algorithm is used as learning algorithm to adjust the weight matrices of the networks. The results show that NN softsensor base on flow rate correlation is easy to build, fast response, no need special instrumentations, better of reliability compare to analyzer reliability, cheaper, low operational cost, low maintenance cost, and has goodRoot Mean Square Error (RMSE)

Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures