2,186 research outputs found
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
Sampled-data fuzzy controller for continuous nonlinear systems
The sampled-data fuzzy control of nonlinear systems is presented. The consequents of the fuzzy controller rules are linear sampled-data sub-controllers. As a result, the fuzzy controller is a weighted sum of some linear sampled-data sub-controllers that can be implemented by a microcontroller or a digital computer to lower the implementation cost. Consequently, a hybrid fuzzy controller consisting of continuous-time grades of memberships and discrete-time sub-controller is obtained. The system stability of the fuzzy control system is investigated on the basis of Lyapunov-based approach. The sampling activity introduces discontinuity to complicate the system dynamics and make the stability analysis difficult. The proposed fuzzy controller exhibits a favourable property to alleviate the conservativeness of the stability analysis. Furthermore, linear matrix inequality-based performance conditions are derived to guarantee the system performance of the fuzzy control system. An application example is given to illustrate the merits of the proposed approac
Hybrid LQG-Neural Controller for Inverted Pendulum System
The paper presents a hybrid system controller, incorporating a neural and an
LQG controller. The neural controller has been optimized by genetic algorithms
directly on the inverted pendulum system. The failure free optimization process
stipulated a relatively small region of the asymptotic stability of the neural
controller, which is concentrated around the regulation point. The presented
hybrid controller combines benefits of a genetically optimized neural
controller and an LQG controller in a single system controller. High quality of
the regulation process is achieved through utilization of the neural
controller, while stability of the system during transient processes and a wide
range of operation are assured through application of the LQG controller. The
hybrid controller has been validated by applying it to a simulation model of an
inherently unstable system of inverted pendulum
A Two-Wheeled Vehicle Navigation System Based on a Fuzzy Logic Controller
The paper deals with a two-wheeled vehicle,namely ESG-2 (Extended Segway-like Generation- 2) navigation control system using a fuzzy logic controller. The vehicle employs two wheels left and right independently which are controlled independently using a fuzzy logic controller respectively. The controllers deal with a compact and implementable application for the normal using with a person (human with 60kg weight in average)
loaded on the vehicle. A modified infrared-based range sensor system is applied to the vehicle as a tilt sensor and it is incorporated with an accelerometer to control its response in case of the dynamics disturbances. The
fuzzy controller runs in tilt-mode while a reference tilt using a potentiometer (as steer system) is taken into account for navigating the vehicle. From the simulation using MATLAB @ and experiments it is obvious that the prototype of ESG-2 is quite challenging to be developed in the future
Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults
The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust Lâ norm fault estimation and robust Lâ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference. Keyword
Evolutionary Networks for Multi-Behavioural Robot Control : A thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University, Albany, New Zealand
Artificial Intelligence can be applied to a wide variety of real world problems, with
varying levels of complexity; nonetheless, real world problems often demand for
capabilities that are difficult, if not impossible to achieve using a single Artificial
Intelligence algorithm. This challenge gave rise to the development of hybrid systems
that put together a combination of complementary algorithms. Hybrid approaches
come at a cost however, as they introduce additional complications for the developer,
such as how the algorithms should interact and when the independent algorithms
should be executed. This research introduces a new algorithm called Cascading
Genetic Network Programming (CGNP), which contains significant changes to the
original Genetic Network Programming. This new algorithm has the facility to
include any Artificial Intelligence algorithm into its directed graph network, as either
a judgement or processing node. CGNP introduces a novel ability for a scalable
multiple layer network, of independent instances of the CGNP algorithm itself. This
facilitates problem subdivision, independent optimisation of these underlying layers
and the ability to develop varying levels of complexity, from individual motor control
to high level dynamic role allocation systems. Mechanisms are incorporated to
prevent the child networks from executing beyond their requirement, allowing the
parent to maintain control. The ability to optimise any data within each node
is added, allowing for general purpose node development and therefore allowing
node reuse in a wide variety of applications without modification. The abilities
of the Cascaded Genetic Network Programming algorithm are demonstrated and
proved through the development of a multi-behavioural robot soccer goal keeper, as
a testbed where an individual Artificial Intelligence system may not be sufficient.
The overall role is subdivided into three components and individually optimised
which allow the robot to pursue a target object or location, rotate towards a target
and provide basic functionality for defending a goal. These three components are
then used in a higher level network as independent nodes, to solve the overall multi-
behavioural goal keeper. Experiments show that the resulting controller defends the
goal with a success rate of 91%, after 12 hours training using a population of 400
and 60 generations
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