Design of Massive Actuators For 3D Robot Manipulators

In this paper, a novel parallel manipulator with discrete control system is developed. An efficient method such as Inverse Static Analysis (ISA) is employed to determine the state of each actuator on parallel manipulator when the position or force of manipulator is already known. The designing a parallel manipulator with 16 actuators which are controlled discretely is a must because the mechanism will use artificial methods in dealing with the ISA problem. In this approach, mathematical model is not required. The research method used simulation software and hardware testing with the case of parallel manipulator with 16 actuators. Simulations with typical desired force inputs are presented and a good performance of the mechanism is obtained. The results showed that the parallel manipulator has the Root Mean Squared Error (RMSE) has less than 3% and can be used for artificial intelligence implementation

Comparison of BPA and LMA Methods for Takagi - Sugeno Type MIMO Neuro-Fuzzy Network to Forecast Electrical Load TIME Series

This paper describes an accelerated Backpropagation algorithm (BPA) that can be used to train the Takagi-Sugeno (TS) type multi-input multi-output (MIMO) neuro-fuzzy network efficiently. Also other method such as accelerated Levenberg-Marquardt algorithm (LMA) will be compared to BPA. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), Mean Squared Error (MSE), and also Root Mean Squared Error (RMSE), down to the desired error goal much faster than that the simple BPA or LMA. Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of Electrical load time series

3D Platform Simulator Design Using Discrete Multi-Piston Actuators

Generally, 3D simulator uses continuous feedback control system to control their actuator states. The objective of this article is to control the movement of actuators in compliance with 3D position required by simulator. This research uses discrete actuator for 3D simulator with four actuators that is open-loop controlled using Neuro-fuzzy control system. The actuator possesses linear pneumatic actuator with three pistons inside where each piston has independent intake. With the proposed design, the actuator able to give degree of discrete not only two (binary) values but also 26 combinations of discrete values. The 3D simulator proposed in this research have four actuators and two passives like-actuator. This configuration gives 4 degrees of freedom of platform movement. Applying force to the actuator using discrete output controller make possible to get more precise in terms of control and movement of the simulator that very useful for many force control applications

Energy Decomposition Model Using Takagi-Sugeno Neuro Fuzzy

Decomposition analysis is useful method to determine significant factors contribute towards the development of energy consumption. This paper presents factors decomposition of electricity consumption in Indonesia’s household sector using artificial intelligent method. The proposed artificial intelligent technique used in this study is the Neuro Fuzzy Takagi-Sugeno (NFTS) network, which is worked under multiple input multiple output condition. By tuning the appropriate Gaussian parameters, which are mean and variance, and two Takagi-Sugeno weight, the changes in electricity consumption that is decomposed into production effect, structural effect, and efficiency effect, has revealed. Compared to the common method, the performance of NFTS network for both constant and current price variables is quite satisfied, given the error generated in the network ranges between 0.003 and 2.09 %, which is quite low and acceptable

3D Platform Simulator Design Using Discrete Multi-Piston Actuators

Generally, 3D simulator uses continuous feedback control system to control their actuator states. The objective of this article is to control the movement of actuators in compliance with 3D position required by simulator. This research uses discrete actuator for 3D simulator with four actuators that is open-loop controlled using Neuro-fuzzy control system. The actuator possesses linear pneumatic actuator with three pistons inside where each piston has independent intake. With the proposed design, the actuator able to give degree of discrete not only two (binary) values but also 26 combinations of discrete values. The 3D simulator proposed in this research have four actuators and two passives like-actuator. This configuration gives 4 degrees of freedom of platform movement. Applying force to the actuator using discrete output controller make possible to get more precise in terms of control and movement of the simulator that very useful for many force control applications

ENHANCED NEURO-FUZZY ARCHITECTURE FOR ELECTRICAL LOAD FORECASTING

Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application. The system uses Gaussian membership function (GMF) for Takagi-Sugeno fuzzy logic system. The training algorithm is Levenberg-Marquardt algorithm to adjust the parameters in order to get better forecasting system than the previous researches. The electrical load was taken from East Java-Bali from September 2005 to August 2007. The architecture uses 4 inputs, 3 outputs with 5 GMFs. The system uses the following parameters: momentum=0.005, gamma=0.0005 and wildness factor=1.001. The MSE for short term forecasting for January to March 2007 is 0.0010, but the long term forecasting for June to August 2007 has MSE 0.0011.

Enhanced Neuro-Fuzzy Architecture for Electrical Load Forecasting

Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application. The system uses Gaussian membership function (GMF) for Takagi-Sugeno fuzzy logic system. The training algorithm is Levenberg-Marquardt algorithm to adjust the parameters in order to get better forecasting system than the previous researches. The electrical load was taken from East Java-Bali from September 2005 to August 2007. The architecture uses 4 inputs, 3 outputs with 5 GMFs. The system uses the following parameters: momentum=0.005, gamma=0.0005 and wildness factor=1.001. The MSE for short term forecasting for January to March 2007 is 0.0010, but the long term forecasting for June to August 2007 has MSE 0.0011. Keywords: forecasting, LMA, neuro-fuzz

KONTROL ROBOT MOBIL PENJEJAK GARIS BERWARNA DENGAN MEMANFAATKAN KAMERA SEBAGAI SENSOR

Makalah ini akan menjelaskan tentang penggunaan kamera sebagai sensor posisi pada kontrol robot mobil penjejak garis berwarna. robot mobil didisain untuk dapat mengikuti sebuah garis berwarna dari sekumpulan garis - garis berwarna yang ada

A Study of Mobile Robot Control using EEG Emotiv Epoc Sensor

The study was using an EEG Emotiv Epoc+ sensor to recognize brain activity for controlling a mobile robots movement. The study used Emotiv Control Panel software for EEG command identification. The commands will be interfaced inside Mind Your OSCs software and processing software which processed inside an Arduino Controller. The output of the Arduino is a movement command (ie. forward, backward, turn left, and turn right). The training methods of the system composed of three sets of thinking mode. First, thinking with doing facial expressions. Second, thinking with visual help. Third, thinking mentally without any help. In the first set, there are two configurations thinking with facial expression help as command of the mobile robot. Final results of the system are the second facial expressions configuration as the best facial expressions method with success rate 88.33 %. The second facial expression configuration has overall response time 1.60175 s faster than the first facial expressions configuration. In these two methods have dominant signals on the frontal lobe. The second facial expressions method have overall respond time 6.12 and 9.53 s faster than thinking with visual, and thinking without help respectively