scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
High Fidelity ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
System P ◽  
Single Machine Infinite Bus

In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik N Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
High Fidelity ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Single Machine Infinite Bus

Abstract In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
High Fidelity ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Single Machine Infinite Bus

In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik N Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Synchronous Generator ◽  
Pole Placement ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Single Machine Infinite Bus

Abstract In this manuscript we present a high fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the higher fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

scholarly journals Mathematical Modelling and Control of a Two-Wheeled PUMA-LikeVehicle

2016 ◽  
Vol 6 (2) ◽  
pp. 11 ◽  
Author(s):  
Khaled M Goher
Keyword(s):  
Mathematical Modelling ◽  
Sliding Mode ◽  
Impact Load ◽  
State Space Model ◽  
Linear Quadratic Regulator ◽  
The Body ◽  
Pole Placement ◽  
General Motors ◽  
Linear Quadratic ◽  
And Control

<p class="1Body">This paper presents mathematical modelling and control of a two-wheeled single-seat vehicle. The design of the vehicle is inspired by the Personal Urban Mobility and Accessibility (PUMA) vehicle developed by General Motors® in collaboration with Segway®. The body of the vehicle is designed to have two main parts. The vehicle is activated using three motors; a linear motor to activate the upper part in a sliding mode and two DC motors activating the vehicle while moving forward/backward and/or manoeuvring. Two stages proportional-integral-derivative (PID) control schemes are designed and implemented on the system models. The state space model of the vehicle is derived from the linearized equations. Controller based on the Linear Quadratic Regulator (LQR) and the pole placement techniques are developed and implemented. Further investigation of the robustness of the developed LQR and the pole placement techniques is emphasized through various experiments using an applied impact load on the vehicle.</p>

scholarly journals Optimal Attack against Autoregressive Models by Manipulating the Environment

2020 ◽  
Vol 34 (04) ◽  
pp. 3545-3552
Author(s):  
Yiding Chen ◽  
Xiaojin Zhu
Keyword(s):  
Predictive Control ◽  
Linear Models ◽  
Nonlinear Models ◽  
Linear Quadratic Regulator ◽  
Black Box ◽  
Combine System ◽  
Linear Quadratic ◽  
Time Series Forecast ◽  
Forecast Models ◽  
Adversarial Attack

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks.

scholarly journals DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

2016 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Osama Elshazly ◽  
Hossam Abbas ◽  
Zakarya Zyada
Keyword(s):  
Mobile Robot ◽  
Inverse Dynamics ◽  
Linear Quadratic Regulator ◽  
Nonlinear Controller ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Control Schemes ◽  
Skid Steering ◽  
Drive Model

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller.

FULL-AND REDUCED- ORDER COMPENSATOR FOR INNOSAT ATTITUDE CONTROL

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Fadzilah Hashim ◽  
Mohd Yusoff Mashor ◽  
Siti Maryam Sharun
Keyword(s):  
State Space ◽  
Attitude Control ◽  
Space Form ◽  
Transfer Functions ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Best Value ◽  
Control Scheme

This paper presents a study on the estimator based on Linear Quadratic Regulator (LQR) control scheme for Innovative Satellite (InnoSAT). By using LQR control scheme, the controller and the estimator has been derived for state space form in all three axes to stabilize the system’s performance. This study starts by converting the transfer functions of attitude control into state space form.  Then, the step continues by finding the best value of weighting matrices of LQR in order to obtain the best value of controller gain, K. After that, the best value of L is obtained for the estimator gain. The value of K and L is combined in forming full order compensator and in the same time the reduced order compensator is also formed. Lastly, the performance of full order compensator is compared to reduced order compensator. From the simulation, results indicate that both types of estimators have presented good stability and tracking performance. However, reduced order estimator has simpler equation and faster convergence to zero than the full order estimator. This property is very important in developing a satellite attitude control for real-time implementation.

Flight Control System Design for Propulsion-Controlled Aircraft

2005 ◽  
Vol 219 (4) ◽  
pp. 329-340 ◽  
Author(s):  
Y Ochi
Keyword(s):  
Flight Control ◽  
Linear Models ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Longitudinal Motion ◽  
Linear Quadratic ◽  
Engine Thrust ◽  
Model Following Control ◽  
Flight Controls

The loss of an aircraft's primary flight controls can lead to a fatal accident. However, if the engine thrust is available, controllability and safety can be retained. This article describes flight control using engine thrust only when an aircraft has lost all primary flight controls. This is a kind of flight control reconfiguration. For safe return, the aircraft must first descend to a landing area, decelerate to a landing speed, and then be capable of precise flight control for approach and landing. For these purposes, two kinds of controllers are required: a controller for descent and deceleration and a controller for approach and landing. The former controller is designed for longitudinal motion using a model-following control method, based on a linear quadratic regulator. The latter is designed by an H∞ state-feedback control method for both longitudinal and lateral-directional motions. Computer simulation is conducted using linear models of the Boeing 747. The results indicate that flight path control, including approach and landing, is possible using thrust only; however, speed control proves more difficult. However, if the horizontal stabilizer is available, the airspeed can be reduced to a safe landing speed.

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