Medical image classification and symptoms detection using neuro fuzzy

The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great potential in this field. Hence, in this project the neuro fuzzy system or ANFIS was applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the tumors

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems

Finite Element Simulation of Microfluidic Biochip for High Throughput Hydrodynamic Single Cell Trapping

In this paper, a microfluidic device capable of trapping a single cell in a high throughput manner and at high trapping efficiency is designed simply through a concept of hydrodynamic manipulation. The microfluidic device is designed with a series of trap and bypass microchannel structures for trapping individual cells without the need for microwell, robotic equipment, external electric force or surface modification. In order to investigate the single cell trapping efficiency, a finite element model of the proposed design has been developed using ABAQUS-FEA software. Based on the simulation, the geometrical parameters and fluid velocity which affect the single cell trapping are extensively optimized. After optimization of the trap and bypass microchannel structures via simulations, a single cell can be trapped at a desired location efficiently

Sliding mode control design for the attitude and altitude of the quadrotor UAV

Recently, the quadrotor unmanned aerial vehicles (UAV) are attracting significant interest from researchers due to its widespread applications, which involve the civilian and military sectors. In this paper, a robust sliding mode control (SMC) algorithm is designed to stabilize the attitude and track the altitude of quadrotor UAV. The switching function in the SMC control law has been replaced by the error function to reduce the chattering influences. The chattering phenomenon is induced by the parameter uncertainties and external disturbances and results in critical issues, for instance, the vibration in the mechanical components. The simulation results of the traditional SMC and feedback linearization (FBL) are used as the benchmark to test and evaluate the performance of the proposed SMC, which proved that the proposed controller outperforms the traditional SMC and FBL controllers

Position and attitude tracking of MAV quadrotor using SMC-based adaptive PID controller

A micro air vehicle (MAV) is physically lightweight, such that even a slight perturbation could affect its attitude and position tracking. To attain better autonomous flight system performance, MAVs require good control strategies to maintain their attitude stability during translational movement. However, the available control methods nowadays have fixed gain, which is associated with the chattering phenomenon and is not robust enough. To overcome the aforementioned issues, an adaptive proportional integral derivative (PID) control scheme is proposed. An adaptive mechanism based on a second-order sliding mode control is used to tune the parameter gains of the PID controller, and chattering phenomena are reduced by a fuzzy compensator. The Lyapunov stability theorem and gradient descent approach were the basis for the automated tuning. Comparisons between the proposed scheme against SMC-STA and SMC-TanH were also made. MATLAB Simulink simulation results showed the overall favourable performance of the proposed scheme. Finally, the proposed scheme was tested on a model-based platform to prove its effectiveness in a complex real-time embedded system. Orbit and waypoint followers in the platform simulation showed satisfactory performance for the MAV in completing its trajectory with the environment and sensor models as perturbation. Both tests demonstrate the advantages of the proposed scheme, which produces better transient performance and fast convergence towards stability

Minimize the prediction error in external consensus problem using gain error ratio formula

This paper discusses the external consensus problem for non-identical networked multi-agent systems (NMAS) with network data loss, considering uniform consecutive data losses (CDL) induced by long periods of transmission failure. A gain error ratio (GER) formula is proposed to determine the appropriate value of coupling gain between agents in order to minimize the computed prediction error caused by the prediction process. Consequently, the consensus performance with prediction control strategy can be improved. The effectiveness of the proposed formula is demonstrated through simulation

DESIGN AND OPTIMIZATION OF BACKSTEPPING CONTROLLER FOR AN UNDERACTUATED AUTONOMOUS QUADROTOR UNMANNED AERIAL VEHICLE

The development of a high performance controller for a quadrotor unmanned aerial vehicle (UAV) is a challenging issue since a quadrotor is an underactuated and a highly unstable nonlinear system. In this paper, the contribution is focused on the design and optimization of a controller for an autonomous quadrotor UAV. Firstly, the dynamic model of the aerial vehicle is mathematically formulated. Then, an optimal backstepping controller (OBC) is proposed. Conventionally, control parameters of a backstepping controller (BC) are often chosen arbitrarily. To this end, it is necessary to invoke a well-established optimization algorithm in order to find the best parameters. Here, the particle swarm optimization (PSO) is utilized as a new key idea to determine the optimal values of the BC parameters. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error (IAE) performance index. Since the control law is derived based on the Lyapunov theorem, the asymptotical stability of the system can be guaranteed. Finally, the efficiency of the proposed OBC is illustrated by implementing several simulation experiments

Stabilization and trajectory tracking control for underactuated quadrotor helicopter subject to wind-gust disturbance

The control of quadrotor helicopter has been a great challenge for control engineers and researchers since quadrotor is an underactuated and a highly unstable nonlinear system. In this paper, the dynamic model of quadrotor has been derived and a so-called robust optimal backstepping control (ROBC) is designed to address its stabilization and trajectory tracking problem in the existence of external disturbances. The robust controller is achieved by incorporating a prior designed optimal backstepping control (OBC) with a switching function. The control law design utilizes the switching function in order to attenuate the effects caused by external disturbances. In order to eliminate the chattering phenomenon, the sign function is replaced by the saturation function. A new heuristic algorithm namely Gravitational Search Algorithm (GSA) has been employed in designing the OBC. The proposed method is evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development. Simulation results show that the proposed ROBC scheme can achieve favorable control performances compared to the OBC for autonomous quadrotor helicopter in the presence of external disturbances

Microfluidic Channel Geometry and Fluid Velocity Investigation for Single Cell Hydrodynamic Trapping

Microfluidic technology has been applied widely for separating and trapping various type of cells. This technology has open ways to study and understand the biological systems, the mechanism of diseases, developing the therapeutic drugs, strategy to cure diseases and also in developing the biomarker for early disease diagnosis. Hydrodynamic cell trapping offers a great opportunity to direct, position, and trap particles or cells in small volume liquids, a crucial requirement for efficient single cell analysis. The challenges in hydrodynamic trapping are the need for control precisely the microfluidic multiple streams and a precise geometry design required to allow successful trapping. To address this limitation, the single cell hydrodynamic trapping finite element simulation was developed to determine the efficiency of single cell traps of variable geometries. A series of simulation studies were performed to analyze the effect of the trap hole size, channel’s height and fluid’s flow profiles to the appropriate for efficient single cell trapping. From the simulation, increasing the trap hole size has resulted in a gradually decreased of the fluid velocity in the trap channel. Furthermore, the fluid velocity in trap channel was found increasing with the increment of the HChannel. Single cell trapping channel with the HHole of 4 μm and HChannel of 15 μm produced the highest velocity in the trap channel compared to other geometry tests. This finite element model could be utilised as a guideline for designing and developing a chip to reduce the costly and time-consuming trialand-error fabrication process