587 research outputs found
Sistem Bongkar-Muat Muatan Truk Menggunakan Lengan Robot Berbasis Arduino-PC Dengan Metode Back Propagation Neural Network
Bongkar-muat merupakan sebuah proses pemindahan suatu barang dari kendaraan pengangkut menuju gudang maupun sebaliknya. Padasektor pergudangan terutama, dalam proses bongkar-muat masih dilakukan dengan cara konvensional. Hal tersebut menjadi kurang efisien dalam penggunaan tenaga kerja karena barang yang dipindahkan jumlahnya tidak sedikit terlebih jika barang tersebut berat. Dari permasalahan diatas untuk mengurangi beban kerja maka dibuatlah sebuah sistem bongkar-muat menggunakan lengan robot untuk proses pengambilan dan peletakan barang. Lengan robot yang terdiri dari base joint, shoulder joint, elbow joint, wrist joint dan vacuum gripper ini menerapkan metode back propagation neural network untuk menentukan sudut pergerakan motor servo pada tiap sendi terhadap koordinat pixel x, y yang dideteksi oleh kamera dengan penentuan lokasi kardus melalui proses klik operator ada hasil capture kamera pada proses bongkar. Hasil pelatihan back propagation pada lengan robot saat proses bongkar muatan mampu melakukan prediksi sudut pergerakan lengan robot dengan baik. Dengan 15 kali percobaan pergerakan lengan robot didapatkan error rata-rata sebesar1.588% pada koordinat x dan 1.982% pada koordinat y. Sehingga lengan robot mampu menuju kardus dan memindahkannya ke atas konveyor dengan baik. Pada penelitian ini dengan muatan sebanyak 24 kardus memiliki tingkat keberhasilan sebesar 100% pada proses bongkar dan 83.33% pada proses muat.
Kata Kunci— bongkar-muat, lengan robot; motor servo;back propagation neural network
Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models
制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576
A Quasi-ARX Model for Multivariable Decoupling Control of Nonlinear MIMO System
This paper proposes a multiinput and multioutput (MIMO) quasi-autoregressive eXogenous (ARX) model and a multivariable-decoupling proportional integral differential (PID) controller for MIMO nonlinear systems based on the proposed model. The proposed MIMO quasi-ARX model improves the performance of ordinary quasi-ARX model. The proposed controller consists of a traditional PID controller with a decoupling compensator and a feed-forward compensator for the nonlinear dynamics based on the MIMO quasi-ARX model. Then an adaptive control algorithm is presented using the MIMO quasi-ARX radial basis function network (RBFN) prediction model and some stability analysis of control system is shown. Simulation results show the effectiveness of the proposed control method
Artificial intelligence in wind speed forecasting: a review
Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values
An adaptive autopilot design for an uninhabited surface vehicle
An adaptive autopilot design for an uninhabited surface vehicle
Andy SK Annamalai
The work described herein concerns the development of an innovative approach to the
design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of
autonomous missions, uninhabited surface vehicles must be able to operate with a minimum
of external intervention. Existing strategies are limited by their dependence on a fixed
model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect
on performance. This thesis presents an approach based on an adaptive model predictive
control that is capable of retaining full functionality even in the face of sudden changes in
dynamics.
In the first part of this work recent developments in the field of uninhabited surface vehicles
and trends in marine control are discussed. Historical developments and different strategies
for model predictive control as applicable to surface vehicles are also explored. This thesis
also presents innovative work done to improve the hardware on existing Springer
uninhabited surface vehicle to serve as an effective test and research platform. Advanced
controllers such as a model predictive controller are reliant on the accuracy of the model to
accomplish the missions successfully. Hence, different techniques to obtain the model of
Springer are investigated. Data obtained from experiments at Roadford Reservoir, United
Kingdom are utilised to derive a generalised model of Springer by employing an innovative
hybrid modelling technique that incorporates the different forward speeds and variable
payload on-board the vehicle. Waypoint line of sight guidance provides the reference
trajectory essential to complete missions successfully.
The performances of traditional autopilots such as proportional integral and derivative
controllers when applied to Springer are analysed. Autopilots based on modern controllers
such as linear quadratic Gaussian and its innovative variants are integrated with the
navigation and guidance systems on-board Springer. The modified linear quadratic
Gaussian is obtained by combining various state estimators based on the Interval Kalman
filter and the weighted Interval Kalman filter.
Change in system dynamics is a challenge faced by uninhabited surface vehicles that result
in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms
are analysed and an innovative, adaptive autopilot based on model predictive control is
designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that
is obtained by combining the advances made to weighted least squares during this research
and is used in conjunction with model predictive control. Successful experimentation is
undertaken to validate the performance and autonomous mission capabilities of the adaptive
autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council
Modeling of Primary Reformer Tube Metal Temperature (TMT)
This report serves to give the details of the development of the project and the steps taken in realising the Modeling of Primary Reformer Tube Metal Temperature (TMT). The main aim in this project is to develop an adequate model
to predict primary reformer TMT based on real-time data obtained from PETRONAS Ammonia Sdn. Bhd. (PASB) plant
Multivariable System Identification of a Continuous Binary Distillation Column
Distillation is a process that is commonly used in industries for separation purpose. A
distillation column is a multivariable system which shows nonlinear dynamic
behavior due to its nonlinear vapor-liquid equilibrium. In order to gain better product
quality and lower energy consumption of the distillation column, an effective model
based control system is needed to allow the process to be operated over a certain
operating range. In control engineering, System Identification is considered as a well
suited approach for developing an approximate model for the nonlinear system. In this
study, System Identification technique is applied to predict the top and bottom
product composition by focusing the temperature of the distillation column. The
process in the column is based on the distillation of a binary mixture of Isopropyl
Alcohol and Acetone. The experimental data obtained from the distillation column
was used for estimation and validation of simulated models. During analysis, different
types of linear and nonlinear models were developed and are compared to predict the
best model which can be effectively used for designing the control system of the
distillation column. Among the linear models such as; Autoregressive with
Exogenous Input (ARX), Autoregressive Moving Average with Exogenous inputs
(ARMAX), Linear State Space (LSS) model and Continuous Process Model were
developed and compared with each other. The results of this comparison reveals that
the perf01mance of LSS model is efficient and hence it was further used to improve
the modeling approach and compared with other nonlinear models. A Nonlinear State
Space (NSS) model was developed by the combination of LSS and Neural Network
(NN) and is compared solely with NN and ANFIS identification model. The
simulation results show that the developed NSS model is well capable of defining the
dynan1ics of the plant based on the best fit criteria and residual performance. In
addition to this, NSS model predicted the best statistical measurement of the nonlinear
system. This approach is helpful for designing the efficient control system for online
separation process of the plant
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