1. Control Methodology 2. Dynamical Systems 3. Applications to Social and Environmental Problems

Dynamics and Control

Detection of Alzheimer's disease (AD) from magnetic resonance images can help neuroradiologists to make decision rapidly and avoid missing slight lesions in the brain. Currently, scholars have proposed several approaches to automatically detect AD. In this study, we aimed to develop a novel AD detection system with better performance than existing systems. 28 ADs and 98 HCs were selected from OASIS dataset. We used inter-class variance criterion to select single slice from the 3D volumetric data. Our classification system is based on three successful components: wavelet entropy, multilayer perceptron, and biogeography-base optimization. The statistical results of our method obtained an accuracy of 92.40 ± 0.83%, a sensitivity of 92.14 ± 4.39%, a specificity of 92.47 ± 1.23%. After comparison, we observed that our pathological brain detection system is superior to latest 6 other approaches.

Single slice based detection for Alzheimer's disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization

Water level sensing: State of the art review and performance evaluation of a low-cost measurement system

Triangular functions (TF) method for the solution of nonlinear Volterra–Fredholm integral equations

A denoising method for a hydropower unit vibration signal based on variational mode decomposition (VMD) and approximate entropy is proposed. The signal was decomposed by VMD into discrete numbers of modes, then the approximate entropy of each mode was computed. These modes were reconstructed according to a preset threshold of the approximate entropy. Finally, the denoising of the hydropower unit vibration signal can be achieved. A simulation signal and real signals of hydropower unit vibration were used to verify the proposed method. The results showed that the proposed method had a good denoising performance and was better than the wavelet transform method in the signal-to-noise ratio (SNR), root mean square error (RMSE) and partial correlation index. It was ideally suited for the online denoising of the hydropower unit vibration signal.

Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy

In the present work, artificial neural network (ANN) has been used to identify a servo-driven inverted pendulum system. The inverted pendulum is a benchmark problem of nonlinear multivariable system with inherent instability. The multi variable system has been considered with servomotor supply voltage as the input and four states of the system being the outputs. An LSVF controller has been used to stabilize the system for identification in closed loop. Here the non linear model of the inverted pendulum has been simulated. The Levenberg-Marquardt back-propagation method has been used for the non linear system identification via Feed-forward Neural Network (FNN). The neural network is trained using the error between the model's outputs and the plant's actual outputs. The results show good match between predicted and actual outputs.

Identification of servo-driven inverted pendulum system using neural network

The presented work demonstrates in steps the way of development and deployment of Finite Dimensional Robust Repetitive Controller (FDRRC) starting from the conventional Repetitive Controller (RC) which is actually an infinite dimensional system. By the virtue of being infinite dimensional RC can track any periodic reference input but many times degrading stability due to the small period mismatch between controller period and actual period of disturbance and also in high frequency applications. To minimize the problem of instability in high frequency, a low pass filter is introduced in the RC loop which in turn limits the infinite dimensional RC in to Finite Dimensional RC (FDRC) and to deal with small period uncertainties, an attempt is taken to explore the design and implementation of multiple loops Robust Repetitive Controller (RRC). Combination of the said strategies results in FDRRC. The new idea of FDRRC is utilized and experienced through the a 1st order system in this paper and every time a comparison is carried out to show the necessity of development in the considered controller models. Finally the paper tries to emphasise over the effectiveness of the FDRRC based on results obtained.

https://ieeexplore.ieee.org/iel7/7190493/7225915/07225945.pdf

Finite dimensional robust repetitive controller for tracking periodic reference input

In this paper, the performance of discrete wavelet transform (DWT) based PID controller is compared to the conventional proportional-integral-derivative (PID) controller applied to Liquid level system (LLS). Here, DWT is used to decompose the actuating signal into different scales. The high scale gives the approximate or low frequency information and low scale gives the detail or high frequency information contained in the actuating signal. Based on this frequency information, the decomposed coefficients of each level are scaled by their respective gains and then added together to generate the final control signal. The choice of wavelet to decompose the error signal is an important factor because the wavelet affects the reconstructed signal quality and the design of the system as a whole. The minimum description length criterion permits to select not only the suitable wavelet filter but also the optimum number of wavelet coefficients retained for the signal reconstruction. Simulation results show that the proposed controller has faster transient response and more satisfactory operation under steady-state conditions with respect to PID controller. Simulation is done in National Instruments LabVIEW software.

Selection of best wavelet for discrete wavelet transform based PID controller connected with liquid level system and its performances analysis

Sliding mode control algorithm is an efficient control strategy to control systems with bounded nonlinearities and parameter uncertainties. However, if the knowledge about the bounds is unknown, sliding mode control fails to guarantee its claimed robustness. The effects of external disturbances, with large magnitudes, can be tackled by using a high value for the controller gain, but with a consequence of increased level of chattering, an unwanted high frequency switching phenomenon occurring in the neighborhood of the sliding surface. In order to mitigate the above mentioned problems, an adaptive law is used to update the controller gain so that the sliding mode controller can track the desired output successfully. This paper demonstrates the design principles of a sliding mode controller with an adaptive law for updating the gain. The gain is so updated that the sliding mode algorithm successfully tracks the desired reference within finite time and does not overestimate the magnitude of external disturbances, thus preventing high level of chattering. The designed controller can track the desired reference value even if the bounds of the external disturbances are unknown. The controller is implemented on a mathematically modeled real time inverted pendulum system and the output responses are obtained. It is concluded that the sliding mode controller with adaptive gain can tackle systems with unknown bounds and also it takes care of the unwanted chattering effect.

Sliding Mode Control Algorithm with Adaptive Gain and Implementation on Inverted Pendulum System

This paper illustrates the development of a system to measure the variation in yarn parameters using image processing with the help of a low cost USB web camera along with a yarn moving arrangement. The complete system comprises of yarn guides and tension control device, motor with gear box, monitor for displaying yarn parameters etc. The system enables to quantify yarn diameter variation, thick/thin places of the yarn in a single unit. The performance of the developed system is free from any fluctuation of ambient temperature or humidity. Different types of yarn (cotton, jute) with wide range of diameter variation are used to verify the system linearity and ascertain its resolution. The available results are verified with that available from a powerful microscope. The measurement of diameter are performed with cut length from 1 mm to 4 mm. Results are available on the monitor showing instantaneous diameter over average diameter calculated for a specific time, counters displaying the number of thick/thin places. The future goal of this work is to acquire the image of the yarn from a spinning frame with the help of a high resolution web camera and to process the same with advanced software to characterise the yarn which in turn dictate the fabric appearance.

Anindita Sengupta

Papers

Identification of servo-driven inverted pendulum system using neural network

Finite dimensional robust repetitive controller for tracking periodic reference input

Selection of best wavelet for discrete wavelet transform based PID controller connected with liquid level system and its performances analysis

Sliding Mode Control Algorithm with Adaptive Gain and Implementation on Inverted Pendulum System

Yarn parameterization based on image processing