Regression between headmaster leadership, task load and job satisfaction of special education integration program teacher

Managing school is a daunting task for a headmaster. This responsibility is exacerbated when it involves the Special Education Integration Program (SEIP). This situation requires appropriate and effective leadership in addressing some of the issues that are currently taking place at SEIP such as task load and job satisfaction. This study aimed to identify the influence of headmaster leadership on task load and teacher job satisfaction at SEIP. This quantitative study was conducted by distributing 400 sets of randomized questionnaires to SEIP teachers across Malaysia through google form. The data obtained were then analyzed using Structural Equation Modeling (SEM) and AMOS software. The results show that there is a significant positive effect on the leadership of the headmaster and the task load of the teacher. Likewise, the construct of task load and teacher job satisfaction has a significant positive effect. However, for the construct of headmaster leadership and teacher job satisfaction, there was no significant positive relationship. This finding is very important as a reference to the school administration re-evaluating their leadership so as not to burden SEIP teachers and to give them job satisfaction. In addition, the findings of this study can also serve as a guide for SEIP teachers to increase awareness of the importance of managing their tasks. This study also focused on education leadership in general and more specifically on special education leadership

Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency

Development of a self balanced robot and its controller

Two wheeled balancing robots are based on inverted pendulum configuration which relies upon dynamic balancing systems for balancing and maneuvering. This project is based on the development of a self-balanced two wheeled robot which has a configuration similar to a bicycle. These robot bases provide exceptional robustness and capability due to their smaller size and power requirements. Outcome of research in this field had led to the birth of robots such as Segway, Murata boy etc. Such robots find their applications in surveillance & transportation purpose. Here, in particular, the focus is on the electro-mechanical mechanisms & control algorithms required to enable the robot to perceive and act in real time for a dynamically changing world. Using an Ultrasonic sensor and an accelerometer we get the information about the tilt of the robot from its equilibrium position. Balancing was done using a servo motor, a DC motor and a control momnt gyroscope. While these techniques are applicable to many robot applications, the construction of sensors, filters and actuator system is a learning experience

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy

Fuzzy logic controller design for inverted pendulum system

Inverted pendulum system is unstable without control, that is, the pendulum wills simply fall over if the cart isn’t moved to balance it and naturally falls downward because of gravity. Thus, the inverted pendulum system is inherently unstable. In order to keep it upright, or stabilize this system, one needs to manipulate it, either vertically or horizontally and it requires a continuous correction mechanism to stay upright since the system is unstable, non-linear and non-minimum phase behavior. To overcome this problem, the fuzzy logic controller will be designed. The Fuzzy Logic Controller has been chosen to stabilize the pendulum rod and keeping the cart in a desired position. Fuzzy logic has provided a simple way without going through the mathematical approach as conventional controller in order to arrive at a definite conclusion based upon nonlinear and an unstable of inverted pendulum system. Besides that, Fuzzy logic control system (FLC) was chosen as the control technique because of its ability to deal with nonlinear systems, as well as its intuitive nature. One special feature of fuzzy logic control is that it utilizes the expertise of humans to control the physical system, so that complex system can be controlled without extensive modeling of the relationship between the input and output of the system

Design and Control of a Two-Wheeled Robotic Walker

This thesis presents the design, construction, and control of a two-wheeled inverted pendulum (TWIP) robotic walker prototype for assisting mobility-impaired users with balance and fall prevention. A conceptual model of the robotic walker is developed and used to illustrate the purpose of this study. A linearized mathematical model of the two-wheeled system is derived using Newtonian mechanics. A control strategy consisting of a decoupled LQR controller and three state variable controllers is developed to stabilize the platform and regulate its behavior with robust disturbance rejection performance. Simulation results reveal that the LQR controller is capable of stabilizing the platform and rejecting external disturbances while the state variable controllers simultaneously regulate the system’s position with smooth and minimum jerk control. A prototype for the two-wheeled system is fabricated and assembled followed by the implementation and tuning of the control algorithms responsible for stabilizing the prototype and regulating its position with optimal performance. Several experiments are conducted, confirming the ability of the decoupled LQR controller to robustly balance the platform while the state variable controllers regulate the platform’s position with smooth and minimum jerk control

Discrete-time neural network based state observer with neural network based control formulation for a class of systems with unmatched uncertainties

An observer is a dynamic system that estimates the state variables of another system using noisy measurements, either to estimate unmeasurable states, or to improve the accuracy of the state measurements. The Modified State Observer (MSO) is a technique that uses a standard observer structure modified to include a neural network to estimate system states as well as system uncertainty. It has been used in orbit uncertainty estimation and atmospheric reentry uncertainty estimation problems to correctly estimate unmodeled system dynamics. A form of the MSO has been used to control a nonlinear electrohydraulic system with parameter uncertainty using a simplified linear model. In this paper an extension of the MSO into discrete-time is developed using Lyapunov stability theory. Discrete-time systems are found in all digital hardware implementations, such as that found in a Martian rover, a quadcopter UAV, or digital flight control systems, and have the added benefit of reduced computation time compared to continuous systems. The derived adaptive update law guarantees stability of the error dynamics and boundedness of the neural network weights. To prove the validity of the discrete-time MSO (DMSO) simulation studies are performed using a two wheeled inverted pendulum (TWIP) robot, an unstable nonlinear system with unmatched uncertainties. Using a linear model with parameter uncertainties, the DMSO is shown to correctly estimate the state of the system as well as the system uncertainty, providing state estimates orders of magnitude more accurate, and in periods of time up to 10 times faster than the Discrete Kalman Filter. The DMSO is implemented on an actual TWIP robot to further validate the performance and demonstrate the applicability to discrete-time systems found in many aerospace applications. Additionally, a new form of neural network control is developed to compensate for the unmatched uncertainties that exist in the TWIP system using a state variable as a virtual control input. It is shown that in all cases the neural network based control assists with the controller effectiveness, resulting in the most effective controller, performing on average 53.1% better than LQR control alone --Abstract, page iii

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Development of a Self Balanced Robot & its Controller

Two wheeled balancing robots are based on inverted pendulum configuration which rely upon dynamic balancing systems for balancing and maneuvering. These robot bases provide exceptional robustness and capability due to their smaller size and power requirements. Outcome of research in this field had led to the birth of robots such as Segway, Murata boy etc. Such robots find their applications in surveillance & transportation purpose. This project is based on development of a self balanced two-wheeled robot which has a configuration similar to a bicycle. In particular, the focus is on the electro-mechanical mechanisms & control algorithms required to enable the robot to perceive and act in real time for a dynamically changing world. While these techniques are applicable to many robot applications, the construction of sensors, filters and actuator system is a learning experience