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E Vinodh Kumar

Bio: E Vinodh Kumar is an academic researcher from VIT University. The author has contributed to research in topics: Engine coolant temperature sensor & Weighting. The author has an hindex of 1, co-authored 2 publications receiving 10 citations.

Papers
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Book ChapterDOI
01 Jan 2016
TL;DR: In this paper, an adaptive particle swarm optimization (APSO) algorithm was employed to solve the weighting matrices selection problem of linear quadratic regulator (LQR).
Abstract: This paper employs an adaptive particle swarm optimization (APSO) algorithm to solve the weighting matrices selection problem of linear quadratic regulator (LQR). One of the important challenges in the design of LQR for real time applications is the optimal choice state and input weighting matrices (Q and R), which play a vital role in determining the performance and optimality of the controller. Commonly, trial and error approach is employed for selecting the weighting matrices, which not only burdens the design but also results in non-optimal response. Hence, to choose the elements of Q and R matrices optimally, an APSO algorithm is formulated and applied for tracking control of inverted pendulum. One of the notable changes introduced in the APSO over conventional PSO is that an adaptive inertia weight parameter (AIWP) is incorporated in the velocity update equation of PSO to increase the convergence rate of PSO. The efficacy of the APSO tuned LQR is compared with that of the PSO tuned LQR. Statistical measures computed for the optimization algorithms to assess the consistency and accuracy prove that the precision and repeatability of APSO is better than those of the conventional PSO.

14 citations

Proceedings ArticleDOI
01 Feb 2019
TL;DR: A mathematical model of the diesel engine is derived using the System Identification Technique and an accurate mathematical model and a precise controller allow the operator to establish the required engine protection parameters such as the coolant temperature, oil pressure and the speed of the engine.
Abstract: This paper deals with the development of a Simulink model for the control, protection and monitoring system of a diesel engine. Various sensors and switches involved in the engine protection and monitoring systems such as throttle position sensor (TPS), lube oil pressure sensor (OPS), coolant temperature sensor (CTS) and idle validation switch (IVS) are discussed in detail. Then, a comparison study of the speed control of the diesel engine was done using P, PI and PID controllers. Finally, a mathematical model of the diesel engine is derived using the System Identification Technique. The results obtained by formulating an accurate mathematical model and a precise controller allow the operator to establish the required engine protection parameters such as the coolant temperature, oil pressure and the speed of the engine

1 citations


Cited by
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Journal ArticleDOI
TL;DR: A method to stabilize a pendulum link in upright position with a focus to minimize the deviation in rotational arm is investigated and a comparison of the performance of the proposed controller with the dynamic particle swarm optimization (dPSO) based fractional controller is presented.
Abstract: Many studies have been conducted by researchers globally in the recent past on the control schemes to enhance the performance of rotary single inverted pendulum (RSIP). The present paper seeks to investigate into a method to stabilize a pendulum link in upright position with a focus to minimize the deviation in rotational arm. Based on the fractional calculus, the proposed control scheme consists of two loop fractional controller in order to intensify the potentiality of RSIP. To tune the parameters of proposed fractional controller a simple graphical tuning method based on frequency response is used. The same parameters will also be tuned using dynamic particle swarm optimization (dPSO). The study will also serve to present a comparison of the performance of the proposed controller with the dynamic particle swarm optimization (dPSO) based fractional controller. Back calculation anti-windup technique will also be incorporated to compensate the saturation non-linearity. Further to confirm the usability of the proposed controller and to avoid the random perturbations sensitivity and robustness is investigated.

28 citations

Journal ArticleDOI
TL;DR: In this article, a method based on Bayesian optimization (BO) was proposed for the automatic selection of weighting matrices for a linear-quadratic regulator (LQR) in order to design an optimal active structural control system.

20 citations

Journal ArticleDOI
TL;DR: An optimal time-varying linear quadratic Gaussian controller (TV-LQG) that is able to overcome the inconsistency between its nonlinear dynamics and the controllers designed based on linearized models is proposed.
Abstract: This paper deals with the holing issue of nonlinear inverted pendulum (IP) system. Close to the equilibrium point, IP is considered as a linear system without disruption and linear control is sufficient. However, when the IP swings over a wide range, its nonlinear dynamics becomes significant and the stabilization of IP becomes challenging task due to the inconsistency between its nonlinear dynamics and the controllers designed based on linearized models. Hence, the need for sophisticated control becomes highly demanding. This paper proposes an optimal time-varying linear quadratic Gaussian controller (TV-LQG) that is able to overcome this inconsistency problem. The proposed TV-LQG utilizes a sigma-point Kalman filter (SPKF) and a linear quadratic regulator with a prescribed degree of stability. SPKF is a highly accurate nonlinear state estimator since it does not use any linearization for calculating the state prediction covariance and Kalman gains. This leads, instantaneously, to a more exact nonlinear state estimation. The estimated states are fed to the Jacobian method, which updates, at once, the system dynamics accordingly. Thus, the parameters of the proposed TV-LQG are optimized based on the updated system dynamics. The proposed controller is intensively tested using simulation experiments and compared to the traditional LQG, LQR self-adjusting control and LQR-fuzzy control. The results proved the robustness and competitiveness of the proposed scheme to enhance the performance of the nonlinear IP system.

4 citations