Optimal control for single-phase UPS inverters based on linear quadratic regulator approach

2006 ◽  
Author(s):  
H. Komurcugil ◽  
O. Kukrer ◽  
A. Doganalp
Keyword(s):  
Optimal Control ◽  
Single Phase ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic

Active Control for Power-Train Vibration of Automotive Using Active Mounts

2013 ◽  
Vol 330 ◽  
pp. 598-601
Author(s):  
Guo Chun Sun ◽  
Li Meng He
Keyword(s):  
Optimal Control ◽  
Vibration Isolation ◽  
Linear Quadratic Regulator ◽  
Active System ◽  
Linear Quadratic ◽  
Isolation System ◽  
Vibration Isolation System ◽  
Engine Vibration ◽  
Active Vibration ◽  
Piezoelectric Stack Actuator

In this work, a new active mount featuring piezostack actuators and a rubber element is proposed and applied to a vibration control system. After describing the configuration and operating principle of the proposed mount, an appropriate rubber element and appropriate piezostacks are designed. Through the analysis of the property of the rubber and piezoelectric stack actuator, a mechanical model of the active vibration isolation system with the active mounts is established. An optimal control algorithm is presented for engine vibration isolation system. the controller is designed according to linear quadratic regulator (LQR) theory. Simulation shows the active system has a better consequence in reducing the vibration of the chassis significantly with respect to the ACM and the optimal control than that in the passive system.

scholarly journals Optimal Control for Single Inverted Pendulum Based on Linear Quadratic Regulator

2016 ◽  
Vol 44 ◽  
pp. 02064
Author(s):  
Huan Xin Cheng ◽  
Jun Xi Chen ◽  
Jing Li ◽  
Li Cheng
Keyword(s):  
Optimal Control ◽  
Inverted Pendulum ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic

Numeric Approach on Optimal Control for the Path Following System in Autonomous Vehicle

2019 ◽  
Author(s):  
Huyao Wu ◽  
Bin Ran
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Autonomous Vehicle ◽  
Path Following ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Linear Quadratic ◽  
The Road ◽  
Lqr Controller ◽  
Dynamic Errors

Abstract In this paper, the control strategies for Path Following System (PFS) in autonomous vehicle, which lets vehicle stay in the center of its lane is discussed, we will create a plant mechanical, mathematical and error dynamics model for the study of PFS, which is stabilized by the state-feedback control law, also considers the output where the sensor is made. We apply mainly an optimal control or configure a Linear-quadratic Regulator (LQR) for state space systems and compare it to that based on the Pole Assignment (PA). Combined with a typical operating scenario of the road, we mainly consider static and dynamic errors in the moving process, and how intensely the error fluctuates and how errors are related to the next time. Figures and data show that the LQR controller successfully adjusts and gives appropriate input to let the vehicle approach to centerline, errors and the steering angle required to negotiate a curved road are presented and analyzed, finally relevant conclusions are drawn.

scholarly journals Active Vibration Control of the Sting Used in Wind Tunnel: Comparison of Three Control Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xing Shen ◽  
Yuke Dai ◽  
Mingxuan Chen ◽  
Lei Zhang ◽  
Li Yu
Keyword(s):  
Optimal Control ◽  
Wind Tunnel ◽  
Vibration Control ◽  
Control Algorithm ◽  
Active Vibration Control ◽  
Linear Quadratic Regulator ◽  
Real Time Control ◽  
Linear Quadratic ◽  
Control Effort ◽  
Active Vibration

In wind tunnel tests, cantilever stings are often used as model-mount in order to reduce flow interference on experimental data. In this case, however, large-amplitude vibration and low-frequency vibration are easily produced on the system, which indicates the potential hazards of gaining inaccurate data and even damaging the structure. This paper details three algorithms, respectively, Classical PD Algorithm, Artificial Neural Network PID (NNPID), and Linear Quadratic Regulator (LQR) Optimal Control Algorithm, which can realize active vibration control of sting used in wind tunnel. The hardware platform of the first-order vibration damping system based on piezoelectric structure is set up and the real-time control software is designed to verify the feasibility and practicability of the algorithms. While the PD algorithm is the most common method in engineering, the results show that all the algorithms can achieve the purpose of over 80% reduction, and the last two algorithms perform even better. Besides, self-tuning is realized in NNPID, and with the help of the Observer/Kalman Filter Identification (OKID), LQR optimal control algorithm can make the control effort as small as possible. The paper proves the superiority of NNPID and LQR algorithms and can be an available reference for vibration control of wind tunnel system.

scholarly journals Optimal vibration control of moving-mass beam systems with uncertainty

2019 ◽  
Vol 39 (3) ◽  
pp. 803-817
Author(s):  
Xiaoxiao Liu ◽  
YuanSheng Wang ◽  
XingMin Ren
Keyword(s):  
Optimal Control ◽  
Moving Load ◽  
Linear Quadratic Regulator ◽  
Uncertain System ◽  
Moving Mass ◽  
Linear Quadratic ◽  
Mass System ◽  
Weighting Matrix ◽  
Probability Density Evolution Method ◽  
Optimal Regulator

A linear optimal regulator for uncertain system is designed through the application of the probability density evolution method to linear quadratic regulator controller. One important background of this work is bridge-vehicle/gun-projectile system. This type of optimal problem is currently transformed into a moving load problem. The developed optimal regulator can provide the law of probability densities of outputs varying with time. In order to make the advocated method reach an optimal performance, the beneficial weighting matrix pair (Q, R) is selected using a trade-off sense. The designed regulator is then applied to a coupled simply supported beam-moving mass system, choosing the mid-span deflection as an output response and considering stochastic system parameters. The numerical example shows that the robustness of the proposed optimal regulator cannot be overestimated in comparison with a deterministic linear quadratic regulator controller. Further, the proposed method can produce an efficient solution channel for modern optimal control theory, especially, when compared with different uncertain optimal control techniques.

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