Stability Enhancement of Single Generator Connected to Grid by Linear Quadratic Regulator

2020 ◽  
Author(s):  
Ganesh P. Prajapat ◽  
Atul Awasthi
Keyword(s):  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Single Generator ◽  
Stability Enhancement

Modeling and Stability Enhancement of Wind Turbine using Linear Quadratic Regulator

2020 ◽  
Author(s):  
Shaharyar Yousaf ◽  
Neelam Mughees ◽  
Abdullah Mughees ◽  
Ali Abbas ◽  
Syed Zulqadar Hassan ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Stability Enhancement

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

2021 ◽  
pp. 095440622110099
Author(s):  
Moataz Ahmed ◽  
Moustafa El-Gindy ◽  
Haoxiang Lang
Keyword(s):  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Rough Terrain ◽  
Lateral Stability ◽  
Linear Quadratic ◽  
Simulation Based ◽  
Combat Vehicle ◽  
Lqr Controller ◽  
Stability Enhancement ◽  
Gain Scheduled

Multi-axle vehicles are widely used in several applications such as transportation, industrial, and military field, because of its higher reliability in comparison with conventional two axles vehicles. Despite that, there is a paucity of research studies that consider lateral stability enhancement of these vehicles, especially on rough terrain. This simulation-based research study fills this gap and introduces a new adaptive Active Rear Steering (ARS) controller that improves the lateral stability of an 8x8 combat vehicle for rough-terrain operation. The developed controller is designed utilizing the Integral Sliding Mode Control theory (ISMC) based on Gain-Scheduled Linear Quadratic Regulator (GSLQR). Besides, the GSLQR control gains are optimized by a Genetic Algorithm (GA) toolbox using a new synthesized cost function to ensure asymptotic stability. Furthermore, a new Adaptive-ISMC (AISMC) is introduced by using genetic programming to generate control equations that can replace the developed high-dimension GSLQR gains and facilitate future hardware implementation. The developed controller is evaluated by performing a series of simulation-based Double Lane Change (DLC) maneuvers on several rough terrains. The evaluation is conducted for both high friction and slippery surfaces at high and moderate speed, consequently. The results show high fidelity and robustness of the developed controller in comparison with a previously designed optimal LQR controller.

Analysis of Synchronous Moment for Active Front Steering and a Two Actuated Wheels of Electric Vehicle Based on Dynamic Stability Enhancement

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Eid. S. Mohamed ◽  
Mh.I. Khalil ◽  
Ahmed A.A. Saad
Keyword(s):  
Linear Quadratic Regulator ◽  
Vehicle Stability ◽  
Linear Quadratic ◽  
Smart Systems ◽  
Active Front Steering ◽  
Lqr Controller ◽  
Optimal Linear ◽  
Vehicle Trajectory ◽  
Stability Enhancement ◽  
Yaw Moment

Active Front Steering (AFS) and Direct Yaw moment Controller (DYC) are the vehicle smart systems to improve the vehicle stability and safety. The AFS uses front wheels Steer-By-Wire (SBW) system. DYC uses Rear Independent in Wheel Actuated Electric Vehicles (RIWA-EVs). It generates yaw moment to correct the vehicle state deviations. The proposed controller algorithm consists of two levels. First level feedback controller evaluate the optimal yaw moment generated to achieve the desired vehicle trajectory motion with minimize the yaw rate and side-slip errors. The second level controller is utilized to allocate the required front steer angle and traction/ regeneration to the RIWA embedded in rear wheels by taking into account the tire slip. An optimal Linear Quadratic Regulator (LQR) controller is designed, and its controller effectiveness is evaluated under various input driving manoeuvres. The results indicate that the integrated AFS/DYC can significantly stabilize the vehicle motion and highly reduce the driver’s workload. The laboratory experiment of AFS subsystem, for adequate actual front steering angle is measured, in order to apply in vehicle model to predict the responses. The results disclose that the RMS can be an effective route to monitor the vehicle stability.

A Study on Linear Quadratic Regulator with the Desired Low-Pass Property

2013 ◽  
Vol 133 (12) ◽  
pp. 2167-2175 ◽  
Author(s):  
Katsuhiko Fuwa ◽  
Satoshi Murayama ◽  
Tatsuo Narikiyo
Keyword(s):  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Low Pass

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

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