Barjeev Tyagi

Optimal control of nonlinear inverted pendulum dynamical system with disturbance input using PID controller & LQR

Optimal response of the controlled dynamical systems is desired hence for that is the optimal control. Linear quadratic regulator (LQR), an optimal control method, and PID control which are generally used for control of the linear dynamical systems have been used in this paper to control the nonlinear dynamical system. The inverted pendulum, a highly nonlinear unstable system is used as a benchmark for implementing the control methods. In this paper the modeling and control design of nonlinear inverted pendulum-cart dynamic system with disturbance input using PID control & LQR have been presented. The nonlinear system states are fed to LQR which is designed using linear state-space model. Here PID & LQR control methods have been implemented to control the cart position and stabilize the inverted pendulum in vertically upright position. The MATLAB-SIMULINK models have been developed for simulation of the control schemes. The simulation results justify the comparative advantages of LQR control methods.

A hybrid segmentation method based on Gaussian kernel fuzzy clustering and region based active contour model for ultrasound medical images

Abstract Segmentation is a very crucial task for the ultrasound medical images due to the presence of various imaging artifacts and noise. This paper presents a hybrid segmentation method for the ultrasound medical images that utilize both the features of the Gaussian kernel induced fuzzy C-means (GKFCM) clustering and active contour model driven by region scalable fitting (RSF) energy function. In this method, the result obtained from the GKFCM method is utilized to initialize the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters used in the curve evolution process. The RSF formulation that is responsible for attracting the contour toward the object boundaries removes the requirement of the re-initialization process. The performance of the proposed method is evaluated by conducting several experiments on both the synthetic and real ultrasound images. Experimental results demonstrate that the proposed method produces better results by successfully detecting the object boundaries and also ensures an improvement in segmentation accuracy compared to others.

Modelling and Simulation for Optimal Control of Nonlinear Inverted Pendulum Dynamical System Using PID Controller and LQR

This paper presents the modelling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using Proportional-Integral-Derivative (PID) controller and Linear Quadratic Regulator (LQR). LQR, an optimal control technique, and PID control method, both of which are generally used for control of the linear dynamical systems have been used in this paper to control the nonlinear dynamical system. The nonlinear system states are fed to LQR which is designed using linear state-space model. Inverted pendulum, a highly nonlinear unstable system is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches at a desired position and the inverted pendulum stabilizes in upright position. The MATLAB-SIMULINK models have been developed for simulation of control schemes. The simulation results justify the comparative advantages of LQR control methods.

Despeckling of ultrasound medical images using nonlinear adaptive anisotropic diffusion in nonsubsampled shearlet domain

Abstract Despeckling is of great interest in ultrasound medical images. The inherent limitations of acquisition techniques and systems introduce the speckles in ultrasound images. These speckles are the main factors that degrade the quality and most importantly texture information present in ultrasound images. Due to these speckles, experts may not be able to extract correct and useful information from the images. This paper presents an edge preserved despeckling approach that combines the nonsubsampled shearlet transform (NSST) with improved nonlinear diffusion equations. As a new image representation method with the different features of localization, directionality and multiscale, the NSST is utilized to provide the effective representation of the image coefficients. The anisotropic diffusion approach is applied to the noisy coarser NSST coefficients to improve the noise reduction efficiency and effectively preserves the edge features. In the diffusion process, an adaptive gray variance is also incorporated with the gradient information of eight connected neighboring pixels to preserve the edges, effectively. The performance of the proposed method is evaluated by conducting extensive simulations using both the standard test images and several ultrasound medical images. Experiments show that the proposed method provides an improvement not only in noise reduction but also in the preservation of more edges as compared to several existing methods.

Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images

Abstract Ultrasound imaging is one of the most important and cheapest instrument used for diagnostic purpose among the clinicians. Due to inherent limitations of acquisition methods and systems, ultrasound images are corrupted by the multiplicative speckle noise that degrades the quality and most importantly texture information present in the ultrasound image. In this paper, we proposed an algorithm based on a new multiscale geometric representation as discrete ripplet transform and non-linear bilateral filter in order to reduce the speckle noise in ultrasound images. Ripplet transform with their different features of anisotropy, localization, directionality and multiscale is employed to provide effective representation of the noisy coefficients of log transformed ultrasound images. Bilateral filter is applied to the approximation ripplet coefficients to improve the denoising efficiency and preserve the edge features effectively. The performance of the proposed method is evaluated by conductive extensive simulations using both synthetic speckled and real ultrasound images. Experiments show that the proposed method provides better results of removing the speckle and preserving the edges and image details as compared to several existing methods.

Cost–benefit analysis for optimal distributed generation placement in distribution systems

This paper presents an optimisation method to determine optimal allocations of distributed generation (DGs) and capacitors based on maximisation of a profit/worth analysis approach. The optimal locations and sizes of DGs and capacitors have been determined by minimising the power distribution loss. This method considers various technical and economic factors such as line losses, sizes of DGs and capacitors at optimal locations, investment costs, operating costs and maintenance costs of DG and capacitor to achieve the objective for a predetermined number. The electricity market price of grid power has been considered to recover initial investment in a specified time period. The improvement in the voltage profile of the system has also been considered in this work. The particle swarm optimisation technique has been used to solve the optimal placement of DGs and capacitors to maximise the profit. The proposed technique is tested on 33-bus and 69-bus test systems.

Artificial neural network based automatic generation control scheme for deregulated electricity market

This paper presents an artificial neural network (ANN) based frequency controller design for multi-area Automatic Generation Control (AGC) scheme in a deregulated electricity market. The effect of Superconducting Magnetic Energy Storage (SMES) unit has also been considered to develop the model. SMES units have been used to the power systems to inject or absorb active power. The effect of generator rate constraint (GRC) has also been considered in developing the multi area AGC model. A three layer feed forward neural network (NN) is proposed for controller design and trained with Back propagation algorithm (BPA). The poolco based transaction can be implemented by optimizing the bids (price & capacity) submitted by the generating companies (Gencos) and distribution companies (Discos). The functioning of the proposed ANN based controller has been demonstrated on a four-area System, and the results have been compared with those obtained by using a Genetic Algorithm based control scheme.

Speckle filtering of ultrasound images using a modified non-linear diffusion model in non-subsampled shearlet domain

Speckle filtering is of great interest for the ultrasound medical images in which various noises and artefacts are introduced because of various limitations of the acquisition systems and techniques. Speckle is a prime factor to degrade the quality and most importantly, texture information present in the ultrasound images. This study presents a despeckling method based on a modified non-linear diffusion model and non-subsampled shearlet transform (NSST). As a new image representation method with the different features of localisation, directionality and multiscale, the NSST is utilised to provide the effective representation of the image coefficients. The modified anisotropic diffusion is applied to the noisy coarser NSST coefficients to improve the denoising efficiency and preserve the edge features effectively. In the diffusion process, the non-local pixel information is incorporated to evaluate the gradient of eight connected neighbouring pixels with an adaptive grey variance. The performance of the proposed method is evaluated for both the standard test and real ultrasound images. Experimental results show that the proposed method produces better results of noise suppression with the preservation of more edges compared with several existing methods.

Predictive model of load and price for restructured power system using neural network

Load and price prediction are an important component in the economic and secures operation of the competitive restructured power system energy market. This paper presents the use of an artificial neural network to half hourly ahead load prediction and half hourly ahead price prediction applications. By using historical weather, load consumption, price and calendar data, a multi-layer feed forward (FF) neural network trained with Back propagation (BP) algorithm was developed for the half hour ahead prediction. The developed algorithm for half hourly prediction has been tested with Australian market data. The result of ANN prediction model is compared with the conventional Multiple Regression (MR) prediction model.

Multi area AGC scheme using imperialist competition algorithm in restructured power system

Display Omitted PID controller for deregulated two area power system (without GRC) and three area power system (with GRC) is designed using ICA.Parameters of PID has also obtained by genetic algorithm.A comparison of ICA obtained PID and GA obtained PID is carried out.Results show the success and validity of ICA-PID over GA-PID. This paper is focused on optimization based design methodology and application of PID controller in restructured, competitive electricity market environment, for AGC problem. The paper compares two search algorithms for designing of PID controller used for AGC in multiarea power system. The optimal parameters of PID controller have been determined with the use of Imperialist Competitive Algorithm (ICA). A deregulated scenario has been considered to develop the model of the multiarea AGC scheme. This paper presents that the ICA tuned PID (ICA-PID) controller can optimally regulate the generators output and can provide the best dynamic response of frequency and tie-line power on a load perturbation. The performance of proposed controller has been checked on 2-area thermal power system and 3-area thermal-hydro power system with the consideration of generation rate constraint (GRC). The results obtained by ICA-PID controller and genetic algorithm tuned (GA-PID) controller have been compared on the basis of performance parameters (settling time and oscillations). It is seen that ICA-PID controller shows the better performance as compared to GA-PID controller.