Results 161 to 170 of about 32,201 (189)
Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation. [PDF]
Sci RepZheng ZA, Ye Z, Zheng X.
europepmc +1 more source
Data-driven vehicle stability control via co-simulation of digital twin and constrained MPC. [PDF]
Sci RepLin M, Zhang Z, Huang M, Ding Y.
europepmc +1 more source
Frequency Shaping-Based Control Framework for Reducing Motion Sickness in Autonomous Vehicles. [PDF]
Sensors (Basel)Lee S, Lee C, Moon C.
europepmc +1 more source
Optimal DMD Koopman Data-Driven Control of a Worm Robot. [PDF]
Biomimetics (Basel)Rahmani M, Redkar S.
europepmc +1 more source
Humans self-organise balance control strategies on a dynamic platform. [PDF]
Sci RepTaleshi N +4 more
europepmc +1 more source
A Multi-Constraint Co-Optimization LQG Frequency Steering Method for LEO Satellite Oscillators. [PDF]
Sensors (Basel)Wang D, Liao W, Liu B, Yu Q.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the ...
Barrett Ames, George Konidaris
openaire +1 more source
We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the ...
Barrett Ames, George Konidaris
openaire +1 more source
LQR for State-Bounded Structural Control
Journal of Dynamic Systems, Measurement, and Control, 1996In order to prevent structural damages, it is more important to bound the vibration amplitude than to reduce the vibration energy. However, in the performance index for linear quadratic regulator (LQR), the instantaneous amplitude of vibration is not minimized.
Chuang, C.-H., Wu, D.-N., Wang, Q.
openaire +2 more sources
2020 American Control Conference (ACC), 2020
We consider the Linear-Quadratic-Regulator (LQR) problem in terms of optimizing a real-valued matrix function over the set of feedback gains. Such a setup facilitates examining the implications of a natural initial-state independent formulation of LQR in designing first order algorithms.
Jingjing Bu +2 more
openaire +1 more source
We consider the Linear-Quadratic-Regulator (LQR) problem in terms of optimizing a real-valued matrix function over the set of feedback gains. Such a setup facilitates examining the implications of a natural initial-state independent formulation of LQR in designing first order algorithms.
Jingjing Bu +2 more
openaire +1 more source
LQR synthesis via eigenstructure assignment
IFAC Proceedings Volumes, 1999Abstract In this paper, a novel relation between the weighting matrix Q in LQR and an eigenstructure of the desired closed-loop system is proposed. The method of a block controller transformation is utilized to develop the scheme. Using the proposed algorithm, the state feedback gain with the desired eigenstructure in the LQR can be obtained.
Jae Weon Choi, Young Bong Seo
openaire +1 more source