Modelling and intelligent control of double-link flexible robotic manipulator

The use of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. A laboratory sized DLFRM moving in horizontal direction is developed and fabricated to represent the actual dynamics of the system. The research utilized neural network as the model estimation. Results indicated that the identification of the DLFRM system using multi-layer perceptron (MLP) outperformed the Elman neural network (ENN). In the controllers’ development, this research focuses on two main parts namely fixed controller and adaptive controller. In fixed controller, the metaheuristic algorithms known as Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC) were utilized to find optimum value of PID controller parameter to track the desired hub angle and supress the vibration based on the identified models obtained earlier. For the adaptive controller, self-tuning using iterative learning algorithm (ILA) was implemented to adapt the controller parameters to meet the desired performances when there were changes to the system. It was observed that self-tuning using ILA can track the desired hub angle and supress the vibration even when payload was added to the end effector of the system. In contrast, the fixed controller degraded when added payload exceeds 20 g. The performance of these control schemes was analysed separately via real-time PC-based control. The behaviour of the system response was observed in terms of trajectory tracking and vibration suppression. As a conclusion, it was found that the percentage of improvement achieved experimentally by the self-tuning controller over the fixed controller (PID-PSO) for settling time are 3.3 % and 3.28 % of each link respectively. The steady state errors of links 1 and 2 are improved by 91.9 % and 66.7 % respectively. Meanwhile, the vibration suppression for links 1 and 2 are improved by 76.7 % and 67.8 % respectively

A new geometrical approach to solve inverse kinematics of hyper redundant robots with variable link length

In this paper a new approach that generates a general algorithm for n-link hyper-redundant robot is presented. This method uses repetitively the basic inverse kinematics solution of a 2- link robot on some virtual links,where the virtual links are defined following some geometric proposition. Thus, it eliminates the mathematical complexity in computing inverse kinematics solution of n-link hyper redundant robot. Further, this approach can handle planar manipulator with variable links eliminating singularity. Numerical simulations for planar hyper redundant models are presented in order to illustrate the competency of the model

Performance Analysis of IOT based Flood Monitoring Framework in Sub-urban

Flooding is the most common natural catastrophe worldwide that occurred without warning which caused great destruction. The government has spent millions of monies on flood mitigation plans. Several systems had been proposed which some of them have been employed around the country. However, it is limited to selected areas due to high installation and maintenance costs. This paper investigates two potential real-time flood monitoring frameworks in the sub-urban area, which are simpler, inexpensive, and require minimum maintenance. The proposed flood monitoring system utilized wireless data transmission system that is either GSM SIM900A module or XBee Pro module. The data was sent to NodeREd for monitoring purposes. From the result, XBee Pro can send more, and faster data as compared to GSM module. The time delay is very much lower for XBee Pro whereby the difference of realtime data captured as compared to the setup time interval that had been set in the system. In terms of consistency and sensitivity, both systems have equal efficacy. In Summary, both systems are adequate to offer monitoring and immediate warning in real-time. However, XBee pro is preferable if the area has internet access. Meanwhile, if the internet service is limited, GSM module can be a good alternative since it does not need Internet access

A New Geometrical Approach to Solve Inverse kinematics of Hyper Redundant Robots with variable link length

In this paper a new approach that generates a general algorithm for n-link hyper-redundant robot is presented. This method uses repetitively the basic inverse kinematics solution of a 2- link robot on some virtual links, where the virtual links are defined following some geometric proposition. Thus, it eliminates the mathematical complexity in computing inverse kinematics solution of n-link hyper redundant robot. Further, this approach can handle planar manipulator with variable links eliminating singularity. Numerical simulations for planar hyper redundant models are presented in order to illustrate the competency of the model

Development of Black Pepper Rotary Drum Dryer System

Rotary drum dryer has been identified as hygienic and practical method to dry black pepper. The quality of black pepper is defined based on the chemical properties and moisture content. This research aims to develop a control system for black pepper rotary drum dryer. The dried pepper should meet the specific 12% moisture content while the heating temperature must be kept below 550C. The requirement of 12% moisture content is equivalent to 30% of the remaining weight of the pepper (final weight). The developed system uses Arduino Mega 2560 REV board as a microcontroller.  A type K thermocouple with MAX6675 thermocouple amplifier and S-type load cells (TAS501) with HX711 load cell amplifier are used as input sensor to microcontroller.  The system keeps measuring the current weight until it hit the targeted final weight. Two set of experiments that are using 500 g and 1500 g of pre-treated pepper were conducted to verify the system. As a result, the dryer was successful to work within the desired temperature and it stop operating just after the samples reached 12% of the moisture content. The finding has proven a shorten of drying time from 4 to 7 days when using the traditional method to the current 3 – 5 hours only when using the developed system. Hence, this is an improved method achieved to a quick drying of the black pepper.  &nbsp

Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network

This article investigates a two-link flexible manipulator (TLFM) that can be modelled utilizing a deep learning neural network. The system was classified under a multiple-input multiple-output (MIMO) system. In the modelling stage of this study, the TLFM dynamic models were divided into single-input single-output (SISO) models. Since coupling impact was assumed to be minimised, the characterizations of TLFM were defined independently in each model. Two discrete SISO models of a flexible two link manipulator were developed using the torque input and the endpoint accelerations of each link. The input-output data pairs were collected from experimental work and utilised to establish the system model. The Long Short-Term Memory (LSTM) algorithm optimised using Particle Swarm Optimization (PSO) was selected as the model structure due to the system's high degree of nonlinearity. The identification of the TLFM system utilizing LSTM optimised by PSO was successful, according to the high-performance result of PSO. Using LSTM-PSO, it is demonstrated that both link 1 and 2 models are accurately identified and that their performance in terms of MSE for links endpoint acceleration 1 and 2 is within a 95% confidence interval

Dynamic Mathematical Modeling and Simulation Study of Small Scale Autonomous Hovercraft

Nowadays, various mechanical, electrical systems or combination of both systems are used tohelp or ease human beings either during the daily life activity or during the worst condition faced by them. The system that can be used to increase human life quality are such as in military operations, pipeline survey, agricultural operations and border patrol. The worst condition that normally faced by human are such as earthquake, flood, nuclear reactors explosion and etc. One of the combinations of both systems is unmanned hovercraft system which is still not thoroughly explored and designed. Hovercraft is a machine that can move on the land surface or water and it is supported by cushion that has high compressed air inside. The cushion is a close canvas and better known as a skirt. A hovercraft moves on most of surfaces either in rough, soft or slippery condition will be developed. The main idea for this project is to develop a dynamic modelling and controller for autonomous hovercraft. The model of the hovercraft will be initially calculated using Euler Lagrange method. The model of the hovercraft is derived using Maple software. The model that is developed then needs to be tested with open loop simulation in the MATLAB/Simulink environment. The LQR controller to regulate the small scale autonomous hovercraft then will be developed and tested with MATLAB

Intelligent PID Controller of Flexible Link Manipulator with Payload

This paper presents the experimental study of intelligent PID controller with the present of payload. The controllers were constructed to optimally track the desired hub angle and vibration suppression of DLFRM. The hub angle and end-point vibration models were identified based on NNARX structure. The results of all developed controllers were analyzed in terms of trajectory tracking and vibration suppression of DLFRM subjected to disturbance. The simulation studies showed that the intelligent PID controllers have provided good performance. Further investigation via experimental studies was carried out. The results revealed that the intelligent PID control structure able to show similar performance up to 20 g of payload hold by the system. Once the payload increased more than 20 g, the performance of the controller degrades. Thus, it can be concluded that, the controllers can be applied in real application, provided the tuning process were carried out with the existence of the maximum payload which will be subjected in the system. The 20 g payload value can act as uncertainty for the controller performance

Kinematic and Dynamic Model Analysis for An Improved Design of Home-Based Wearable Lower Limb Rehabilitation Robot

This paper presents the analysis of the kinematic and dynamic modeling of a wearable lower-limb robot (WLLR) for home-based applications. SOLIDWORKS software was utilized, and the concept of modular configuration was adopted into the design. The Denavit Hartenberg (DH) and geometric methods were employed to obtain the theoretical of forward and inverse kinematics models respectively. The Lagrangian formulation of the kinetic and potential energy method was selected to derive the joint torque equation. For validation, MATLAB Robotic ToolBox was utilized to simulate forward and inverse kinematic behaviour of the WLLR while MATLAB SimMechanics was used to investigate the maximum torque for the hip and knee joint in various ranges of motion (ROM) and walking conditions. The results showed a strong agreement of the simulation and the theoretical model for the forward and inverse kinematic of WLLR. The torque required for the hip and knee joints in walking conditions was less than the torque required in various ranges of motion. The maximum torque recorded at the hip and knee in ROM condition is 74.73 Nm and 15.05 Nm respectively while the maximum torque recorded for the hip and knee in the walking condition is 55.00 Nm and 11.01 Nm respectively. This analysis is essential as a basis for further actuator selection and control system development

Data Analysis of Fly Ash Geopolymer Compressive Strength Using Machine Learning Method

Geopolymer is an alternative material that is suitable to substitute Ordinary Portland Cement (OPC) to produce concrete. A mixture of geopolymer paste that binds coarse and fine aggregate and other unreacted materials together is called Geopolymer Concrete. Previous studies stated that alkaline activator molarity, water binder ratio, and type of activator played a significant role in the compressive strength of geopolymer concrete. Machine learning or artificial neural networks are particularly appropriate for modelling non-linear relationships, and they are characteristically used to accomplish pattern recognition and categorize objects or signals in vision, speech, and control systems. This research is to analyze compressive strength data sets of geopolymer concrete by using the machine learning method. The result comparison of compressive strength is divided into three parameters which are based on molarity, water binder ratio, and the type of activators in the ratio between sodium hydroxide (NaOH) and sodium silicate (Na2SiO3). The materials used for the preparation of geopolymer concrete in this study are fly ash as a binder, fine and coarse aggregates, water, sodium silicate sodium hydroxide, and (NaOH) (Na2SiO3) as activators. A total of 240 samples were cast and cured at 80 oC for 24 hours with 28 days age of maturity before it’s have been tested for the compressive strength. This study confirms that the molarity, water binding ratio, and the type of activator pointedly affect the compressive strength of geopolymer concrete. The compressive strength was further analyzed by MATLAB to observe the neural network and clustering of the compressive strength data. It is found that there are 9 clusters discovered. The clustering of the compressive strength shows that there is a likeness of material usage in the creation of Geopolymer Concrete