Development of Controller for Arm Exoskeleton

The use of robotics in rehabilitation of stroke patients has not been extensively researched yet. Many studies were performed on the rehabilitation of the upper extremities using arm exoskeleton; the results shown by these studies show a positive effect in the rehabilitation of patients. This project is concerned with performing a study on two different controllers for the arm in order to provide an optimized controller for use in an arm exoskeleton as well as to study the most effective control technique

Genetic algorithm design of neural network and fuzzy logic controllers

Genetic algorithm design of neural network and fuzzy logic controller

Advanced Signal Processing and Control in Anaesthesia

This thesis comprises three major stages: classification of depth of anaesthesia (DOA); modelling a typical patient’s behaviour during a surgical procedure; and control of DOAwith simultaneous administration of propofol and remifentanil. Clinical data gathered in theoperating theatre was used in this project. Multiresolution wavelet analysis was used to extract meaningful features from the auditory evoked potentials (AEP). These features were classified into different DOA levels using a fuzzy relational classifier (FRC). The FRC uses fuzzy clustering and fuzzy relational composition. The FRC had a good performance and was able to distinguish between the DOA levels. A hybrid patient model was developed for the induction and maintenance phase of anaesthesia. An adaptive network-based fuzzy inference system was used to adapt Takagi-Sugeno-Kang (TSK) fuzzy models relating systolic arterial pressure (SAP), heart rate (HR), and the wavelet extracted AEP features with the effect concentrations of propofol and remifentanil. The effect of surgical stimuli on SAP and HR, and the analgesic properties of remifentanil were described by Mamdani fuzzy models, constructed with anaesthetist cooperation. The model proved to be adequate, reflecting the effect of drugs and surgical stimuli. A multivariable fuzzy controller was developed for the simultaneous administration of propofol and remifentanil. The controller is based on linguistic rules that interact with three decision tables, one of which represents a fuzzy PI controller. The infusion rates of the two drugs are determined according to the DOA level and surgical stimulus. Remifentanil is titrated according to the required analgesia level and its synergistic interaction with propofol. The controller was able to adequately achieve and maintain the target DOA level, under different conditions. Overall, it was possible to model the interaction between propofol and remifentanil, and to successfully use this model to develop a closed-loop system in anaesthesia

Serangga dan mitos suku kaum jakun, Kampung Peta, Mersing Johor

This study focuses on seeing insects from the mythical perspective of the Orang Asli tribe of Jakun, Kampung Peta, Mersing Johor. The existence of insects in the life of every ethnic in Malaysia has brought various elements of myths. Therefore, when combining myths and insects, it could be said that myth is a human way of understanding, expressing and linking insects to him/herself as well as a group/culture. The practice of using insects among ethnic groups in daily life is called etnoentomology. In this study, the insects studied are the butterfly (Lepidoptera), the odonates (Odonata) and the cicadas (Homoptera). This is because these insects are very popular in the community and have their own myths that are brought into the local culture of belief

A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented

Dynamic process modeling and hybrid intelligent control of ethylene copolymerization in gas phase catalytic fluidized bed reactors

BACKGROUND: Polyethylene (PE) is the most extensively consumed plastic in the world, and gas phase‐based processes are widely used for its production owing to their flexibility. The sole type of reactor that can produce PE in the gas phase is the fluidized bed reactor (FBR), and effective modeling and control of FBRs are of great importance for design, scale‐up and simulation studies. This paper discusses these issues and suggests a novel advanced control structure for these systems. RESULTS: A unified process modeling and control approach is introduced for ethylene copolymerization in FBRs. The results show that our previously developed two‐phase model is well confirmed using real industrial data and is exact enough to further develop different control strategies. It is also shown that, owing to high system nonlinearities, conventional controllers are not suitable for this system, so advanced controllers are needed. Melt flow index (MFI) and reactor temperature are chosen as vital variables, and intelligent controllers were able to sufficiently control them. Performance indicators show that advanced controllers have a superior performance in comparison with conventional controllers. CONCLUSION: Based on control performance indicators, the adaptive neuro‐fuzzy inference system (ANFIS) controller for MFI control and the hybrid ANFIS–proportional‐integral‐differential (PID) controller for temperature control perform better regarding disturbance rejection and setpoint tracking in comparison with conventional controllers. © 2019 Society of Chemical Industr

Load frequency control in an AC microgrid

In the present scenario, due to the staggering increase in demand for electrical energy, system stability becomes an important concern. When electrical power demand increases, it creates unnecessary frequency oscillation and voltage oscillation in the power system, and hence a lot of pressure are put on it. The increase in the complexity of the power system and connection of various sources, renewable energy can be considered as one of the best alternative source. In our complex grid systems, now a days very much increase in the number of micro-grids (MGs), the existence of a sudden and random load perturbation, uncertainty in the parameter and due to variation in the structure, the operating frequency is not the same as the required frequency. For the purpose of reduction in the frequency deviation and maintaining balance between power generated and the load, our grid system requires an intelligent, sophisticated and easily controllable at optimized point. In this age obscure controllers are not efficient enough to facilitate a wide range of operation. For this purpose, this thesis presents an integration of both fuzzy logic and conventional PI controller with various techniques for optimal tuning of our controllers in the AC micro grid systems. The tuning of PI controller and its control parameters are obtained from Ziegler-Nichols tuning method to optimized the parameter. The systems including the transfer function models are designed and simulated in a user friendly and most reliable MATLAB/Simulink environment

HYBRID FUZZY CONTROL AND ANT COLONY OPTIMIZATION BASED PATH PLANNING FOR WHEEL MOBILE ROBOT NAVIGATION

Wheeled Mobile Robot (WMR) is extremely important for active target tracking control and reactive obstacle avoidance in an unstructured environment. A WMR needs the best control performance an automatic path planning to maintain a very high level of accuracy. Therefore, the development of control strategies and path planning is very significant. Hence, research was carried out to investigate the control and path planning issues of WMR in dynamic environment. Several controllers such as conventional controller Proportional (P), Integral (I), Derivative (D) and Fuzzy Logic controller were investigated. A Hybrid Controller for differential WMR was proposed. Various aspects of the research on WMR such as kinematics model, conventional controller, fuzzy controller and hybrid controller were discussed. Overall it was found that on average the Hybrid Controller gives the best performance with 5.5s, 5.4s and 11s for target of 10x 10y, 30x10y and 60x20y respectively