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

Control techniques over the decades have evolved from the various aspects of Model-Based Control (MBC) to Data Driven Control (DDC). In stark contrast to the model based paradigm which is targeted at addressing the fundamental physics driving the system and intends to freely determine the process transfer function. The data-driven approach instead connotes to ascertaining the process parameters of a system, void of a specified architecture by measuring the input and output data. The present investigation targets a validatory execution of a simple feedback DDC architecture on the position control of a DC motor module. The input-output data obtained from an onboard potentiometer is logged to the host PC using a low cost acquisition set-up and the corresponding model structure is identified using Matlab System Identification Toolbox. Based on the identified plant model, an appropriately tuned PID scheme is proposed that can represent the original hardware response with acceptable fidelity. The ability of the proposed control scheme is augmented by the introduction of standard repetitive control strategy in order to reduce Steady-State tracking errors of the system while negotiating periodic inputs. The experimental results demonstrate the effectiveness of the proposed scheme in offering a highly accurate asymptotic tracking ability.

Position Control of a DC Motor System for Tracking Periodic Reference Inputs in a Data Driven Paradigm 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)

The paper introduces a novel modular estimation approach for lateral vehicle and tire dynamics using a simplified vehicle model and a non-linear estimation algorithm. A dynamics-oriented representa...

Modular estimation of lateral vehicle dynamics and application in optimal AFS control

Selection of Tuning Parameter Ranges for PID Controller Design of MIMO Systems Using ETF Techniques

In this paper wavelet transform is used to determine the state and trajectory sensitivity of homogeneous and non-homogeneous systems. The differential-algebraic equation describing a system is converted via wavelet transform to an algebraic generalized Lyapunov equation which is solved for the coefficients of the state variables in terms of Haar basis. Further, problems of trajectory sensitivity analysis for singular as well as nonsingular systems have also been explored using the same orthogonal basis. Finally, using Kronecker product method, a generalized program is developed to determine the state and sensitivity for any number of basis function in Haar domain.

Wavelet transform for determination of state and trajectory sensitivity of a singular control system

This paper presents the application of Finite Dimensional Repetitive Control (FDRC) strategy along with Proportional-Derivative (PD) and Proportional Integral Derivative (PID) controller to an Automatic Voltage Regulator (AVR) for tracking of the periodic reference signal or rejecting periodic disturbances with a known period. In various laboratories and Industrial applications, AVR has mostly used equipment which needs to provide steady regulated output in presence of different fluctuating signals. The whole effort is consist of modeling and order reduction, validated through fitness test with an actual model using standard system identification toolbox, of AVR and development of FDRC. In FDRC, a special type of Repetitive Control (RC), the problem of instability in case of RC occurred due to an infinite number of high-frequency poles, is minimized by introducing a low pass filter in the RC loop which in turn nullifies the effects of high-frequency poles of RC. The developed FDRC system subjected to periodic reference input is applied to reduced order AVR model in the absence of periodic disturbances as well as in the presence of periodic disturbances. Comparative study of conventional controllers and FDRC based PD and PID controller in terms of performances in both time and frequency domains are done to validate the effectiveness of the FDRC over the conventional ideas.

Anindita Sengupta

Papers

Position Control of a DC Motor System for Tracking Periodic Reference Inputs in a Data Driven Paradigm 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)

Modular estimation of lateral vehicle dynamics and application in optimal AFS control

Selection of Tuning Parameter Ranges for PID Controller Design of MIMO Systems Using ETF Techniques

Wavelet transform for determination of state and trajectory sensitivity of a singular control system

System Identification of an AVR and it’s Control using Finite Dimensional Repetitive Controller