scholarly journals Design and Simulation of a Steam Turbine Generator using Observer Based and LQR Controllers

2020 ◽  
Author(s):  
mustefa jibril ◽  
Messay Tadese ◽  
Eliyas Alemayehu
Keyword(s):  
Steam Turbine ◽  
Performance Improvement ◽  
Linear Quadratic Regulator ◽  
Electrical Energy ◽  
Open Loop ◽  
Loop System ◽  
Linear Quadratic ◽  
Turbine Generator ◽  
Open Loop System ◽  
Lqr Controller

A steam turbine generator is an electromechanical system which converts heat energy to electrical energy. In this paper, the modelling, design and analysis of a simple steam turbine generator have done using Matlab/Simulink Toolbox. The open loop system have been analyzed to have an efficiency of 76.92 %. Observer based & linear quadratic regulator (LQR) controllers have been designed to improve the generating voltage. A comparison of this two proposed controllers have been done for increasing the performance improvement to generate a 220 DC volt. The simulation result shows that the steam turbine generator with observer based controller has a small percentage overshoot with minimum settling time than the steam turbine generator with LQR controller and the open loop system. Finally, the steam turbine generator with observer based controller shows better improvement in performance than the steam turbine generator with LQR controller.

scholarly journals A Hyperbolic Tangent Adaptive PID + LQR Control Applied to a Step-Down Converter Using Poles Placement Design Implemented in FPGA

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Marcelo Dias Pedroso ◽  
Claudinor Bitencourt Nascimento ◽  
Angelo Marcelo Tusset ◽  
Maurício dos Santos Kaster
Keyword(s):  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Open Loop ◽  
Linear Quadratic ◽  
Hyperbolic Tangent ◽  
Dsp Builder ◽  
Loop Transfer Function ◽  
Lqr Controller ◽  
Step Down ◽  
Poles Placement

This work presents an adaptive control that integrates two linear control strategies applied to a step-down converter: Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controls. Considering the converter open loop transfer function and using the poles placement technique, the designs of the two controllers are set so that the operating point of the closed loop system presents the same natural frequency. With poles placement design, the overshoot problems of the LQR controller are avoided. To achieve the best performance of each controller, a hyperbolic tangent weight function is applied. The limits of the hyperbolic tangent function are defined based on the system error range. Simulation results using the Altera DSP Builder software in a MATLAB/SIMULINK environment of the proposed control schemes are presented.

Robust stabilization control of a spatial inverted pendulum using integral sliding mode controller

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ishan Chawla ◽  
Vikram Chopra ◽  
Ashish Singla
Keyword(s):  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Stabilization ◽  
Linear Quadratic Regulator ◽  
Humanoid Robots ◽  
Sliding Mode Controller ◽  
Linear Quadratic ◽  
Integral Sliding Mode ◽  
Wide Range ◽  
Lqr Controller

AbstractFrom the last few decades, inverted pendulums have become a benchmark problem in dynamics and control theory. Due to their inherit nature of nonlinearity, instability and underactuation, these are widely used to verify and implement emerging control techniques. Moreover, the dynamics of inverted pendulum systems resemble many real-world systems such as segways, humanoid robots etc. In the literature, a wide range of controllers had been tested on this problem, out of which, the most robust being the sliding mode controller while the most optimal being the linear quadratic regulator (LQR) controller. The former has a problem of non-robust reachability phase while the later lacks the property of robustness. To address these issues in both the controllers, this paper presents the novel implementation of integral sliding mode controller (ISMC) for stabilization of a spatial inverted pendulum (SIP), also known as an x-y-z inverted pendulum. The structure has three control inputs and five controlled outputs. Mathematical modeling of the system is done using Euler Lagrange approach. ISMC has an advantage of eliminating non-robust reachability phase along with enhancing the robustness of the nominal controller (LQR Controller). To validate the robustness of ISMC to matched uncertainties, an input disturbance is added to the nonlinear model of the system. Simulation results on two different case studies demonstrate that the proposed controller is more robust as compared to conventional LQR controller. Furthermore, the problem of chattering in the controller is dealt by smoothening the controller inputs to the system with insignificant loss in robustness.

scholarly journals Assessment of adaptive capacity to sea level rise using open-loop system, case study: Cirebon and Pangandaran

2021 ◽  
Vol 708 (1) ◽  
pp. 012104
Author(s):  
I Fauzi ◽  
I M Radjawane ◽  
H Latief ◽  
Randy F Ritonga ◽  
H Y Faizin
Keyword(s):  
Sea Level Rise ◽  
Adaptive Capacity ◽  
Sea Level ◽  
Open Loop ◽  
Loop System ◽  
Open Loop System

Multi-Stage System Identification of a Gas Turbine

2017 ◽  
Author(s):  
Amit Pandey ◽  
Maurício de Oliveira ◽  
Chad M. Holcomb
Keyword(s):  
Gas Turbine ◽  
Closed Loop ◽  
Open Loop ◽  
Loop System ◽  
Closed Loop Control ◽  
Input Output ◽  
Open Loop System ◽  
Loop Control ◽  
Multi Stage ◽  
Identification Techniques

Several techniques have recently been proposed to identify open-loop system models from input-output data obtained while the plant is operating under closed-loop control. So called multi-stage identification techniques are particularly useful in industrial applications where obtaining input-output information in the absence of closed-loop control is often difficult. These open-loop system models can then be employed in the design of more sophisticated closed-loop controllers. This paper introduces a methodology to identify linear open-loop models of gas turbine engines using a multi-stage identification procedure. The procedure utilizes closed-loop data to identify a closed-loop sensitivity function in the first stage and extracts the open-loop plant model in the second stage. The closed-loop data can be obtained by any sufficiently informative experiment from a plant in operation or simulation. We present simulation results here. This is the logical process to follow since using experimentation is often prohibitively expensive and unpractical. Both identification stages use standard open-loop identification techniques. We then propose a series of techniques to validate the accuracy of the identified models against first principles simulations in both the time and frequency domains. Finally, the potential to use these models for control design is discussed.

scholarly journals FULL CAR ACTIVE DAMPING SYSTEM FOR VIBRATION CONTROL

2020 ◽  
Vol 6 (4) ◽  
pp. 1-17
Author(s):  
G. Yakubu ◽  
G. Sani ◽  
S. B. Abdulkadir ◽  
A. A.Jimoh ◽  
M. Francis
Keyword(s):  
Mathematical Model ◽  
Linear Quadratic Regulator ◽  
Active Damping ◽  
Settling Time ◽  
Linear Quadratic ◽  
Body Acceleration ◽  
The Road ◽  
Road Profile ◽  
Lqr Controller ◽  
Mass Displacement

Full car passive and active damping system mathematical model was developed. Computer simulation using MATLAB was performed and analyzed. Two different road profile were used to check the performance of the passive and active damping using Linear Quadratic Regulator controller (LQR)Road profile 1 has three bumps with amplitude of 0.05m, 0.025 m and 0.05 m. Road profile 2 has a bump with amplitude of 0.05 m and a hole of -0.025 m. For all the road profiles, there were 100% amplitude reduction in Wheel displacement, Wheel deflection, Suspension travel and body displacement, and 97.5% amplitude reduction in body acceleration for active damping with LQR controller as compared to the road profile and 54.0% amplitude reduction in body acceleration as compared to the passive damping system. For the two road profiles, the settling time for all the observed parameters was less than two (2) seconds. The present work gave faster settling time for mass displacement, body acceleration and wheel displacement.

scholarly journals The linear quadratic regular algorithm-based control system of the direct current motor

2019 ◽  
Vol 10 (2) ◽  
pp. 768
Author(s):  
Trong-Thang Nguyen
Keyword(s):  
Control System ◽  
Direct Current ◽  
State Space Model ◽  
Linear Quadratic Regulator ◽  
Response Speed ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Mathematical Equations ◽  
Feed Forward Controller

<span>This research aims to propose an optimal controller for controlling the speed of the Direct Current (DC) motor. Based on the mathematical equations of DC Motor, the author builds the equations of the state space model and builds the linear quadratic regulator (LQR) controller to minimize the error between the set speed and the response speed of DC motor. The results of the proposed controller are compared with the traditional controllers as the PID, the feed-forward controller. The simulation results show that the quality of the control system in the case of LQR controller is much higher than the traditional controllers. The response speed always follows the set speed with the short conversion time, there isn't overshoot. The response speed is almost unaffected when the torque impact on the shaft is changed.</span>

scholarly journals Simulation of a Steam Injected Gas Turbine Plant Control System

1997 ◽  
Author(s):  
Shusheng Zang ◽  
Jaqiang Pan
Keyword(s):  
Gas Turbine ◽  
Flow Rate ◽  
Controller Design ◽  
Dynamic Performance ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Turbine Plant ◽  
Fuel Flow ◽  
Lqr Controller ◽  
Gas Turbine Plant

The design of a modern Linear Quadratic Regulator (LQR) is described for a test steam injected gas turbine (STIG) unit. The LQR controller is obtained by using the fuel flow rate and the injected steam flow rate as the output parameters. To meet the goal of the shaft speed control, a classical Proportional Differential (PD) controller is compared to the LQR controller design. The control performance of the dynamic response of the STIG plant in the case of rejection of load is evaluated. The results of the computer simulation show a remarkable improvement on the dynamic performance of the STIG unit.

Real-Time Control of a Rotary Inverted Pendulum using Robust LQR-based ANFIS Controller

2018 ◽  
Vol 19 (3-4) ◽  
pp. 379-389 ◽  
Author(s):  
Ishan Chawla ◽  
Ashish Singla
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Inference System ◽  
Wide Range ◽  
Lqr Controller ◽  
Rotary Inverted Pendulum

AbstractFrom the last five decades, inverted pendulum (IP) has been considered as a benchmark problem in the control literature due to its inherit nature of instability, non-linearity and underactuation. Its applicability in wide range of practical systems, demands the need of a robust controller. It is found in the literature that wide range of controllers had been tested on this problem, out of which the most robust being sliding mode controller while the most optimal being linear quadratic regulator (LQR) controller. The former has a problem of discontinuity and chattering, while the latter lacks the property of robustness. To address the robustness issue in LQR controller, this paper proposes a novel robust LQR-based adaptive neural based fuzzy inference system controller, which is a hybrid of LQR and fuzzy inference system. The proposed controller is designed and implemented on rotary inverted pendulum. Further, to validate the robustness of proposed controller to parametric uncertainties, pendulum mass is varied. Simulation and experimental results show that as compared to LQR controller, the proposed controller is robust to variations in pendulum mass and has shown satisfactory performance.

Fretting Behavior of Coated and Treated Titanium Alloy Using an Open-Loop System

2005 ◽  
Author(s):  
G. R. Yantio Njankeu ◽  
J.-Y. Paris ◽  
J. Denape ◽  
L. Pichon ◽  
J.-P. Rivie`re
Keyword(s):  
Titanium Alloy ◽  
Relative Motion ◽  
High Frequency ◽  
Open Loop ◽  
Loop System ◽  
Test Apparatus ◽  
Open Loop System ◽  
Wear Rates ◽  
Low Wear

Titanium alloys are well known to present poor sliding behaviour and high wear values. Various coatings and treatments have been tested to prevent such an occurrence under fretting conditions at high frequency of displacement (100 Hz). An original test apparatus, using an open-loop system instead of a classical imposed displacement simulator, has been performed to directly display the phenomenon of seizure, defined as the stopping of the relative motion between the contacting elements. A classification of the tested coatings has been proposed on the basis of their capacity to maintain full or partial sliding conditions, to present low wear rates and to prevent seizure.

Design and Analysis of Linear Quadratic Regulator for a Non-Linear Positioning System

2015 ◽  
Vol 761 ◽  
pp. 227-232 ◽  
Author(s):  
Tang Teng Fong ◽  
Zamberi Jamaludin ◽  
Ahmad Yusairi Bani Hashim ◽  
Muhamad Arfauz A. Rahman
Keyword(s):  
Physical System ◽  
Inverted Pendulum ◽  
Linear Quadratic Regulator ◽  
Upright Position ◽  
System Model ◽  
Optimum Control ◽  
Control Vector ◽  
Linear Quadratic ◽  
Non Linear ◽  
Lqr Controller

The control of rotary inverted pendulum is a case of classical robust controller design of non-linear system applications. In the control system design, a precise system model is a pre-requisite for an enhanced and optimum control performance. This paper describes the dynamic system model of an inverted pendulum system. The mathematical model was derived, linearized at the upright equilibrium points and validated using non-linear least square frequency domain identification approach based on measured frequency response function of the physical system. Besides that, a linear quadratic regulator (LQR) controller was designed as the balancing controller for the pendulum. An extensive analysis was performed on the effect of the weighting parameter Q on the static time of arm, balance time of pendulum, oscillation, as well as, response of arm and pendulum, in order to determine the optimum state-feedback control vector, K. Furthermore, the optimum control vector was successfully applied and validated on the physical system to stabilize the pendulum in its upright position. In the experimental validation, the LQR controller was able to keep the pendulum in its upright position even in the presence of external disturbance forces.

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