Results 101 to 110 of about 12,702 (221)
OTUD1 Prevents Immunosuppression via Promoting Lysosomal‐Associated Degradation of PD‐L1
MedComm, Volume 7, Issue 11, November 2026.OTUD1 interacts with AKT and decreases AKT‐mTOR pathway activity, facilitating the translocation of TFEB to the nucleus, thereby triggering autophagy‐dependent degradation of PD‐L1 in lysosomes. . ABSTRACT Upregulation of PD‐L1 on tumor cells is a pivotal aspect of immune evasion, leading to treatment resistance, tumor recurrence, and metastasis ...
Yang Hao +10 more
wiley +1 more source
Enhanced LQR Control for Unmanned Helicopter Based on Active Model
, 2007 为了解决无人直升机控制问题,通过把主动建模与LQR(Linear Quadratic Regulator)控制相结合,提出一种能补偿模型差的控制方法。该方法在悬停状态下,采用简化模型设计LQR控制器,并通过UKF(Un-scented-Kalman-Filter)在线估计简化模型与全状态模型的模型差,使用模型差作为补偿项对LQR控制增强。针对实际直升机动力学模型进行仿真,验证了基于UKF的估计和增强LQR控制的有效性。仿真实验结果证明,基于UKF的主动建模技术能够快速估计状态和参数变化 ...
赵新刚 +4 more
core
Stochastic Model Predictive Control Using Indirect Feedback With a Modified Terminal Constraint
Optimal Control Applications and Methods, Volume 47, Issue 6, Page 1411-1422, November/December 2026.This paper proposes a novel dynamic terminal set approach for the model predictive control (MPC) design. It concerns the class of linear systems subject to stochastic disturbances and builds upon an indirect feedback (IF) strategy. The main contribution is to explore the time‐varying definition of the constraints in the indirect feedback SMPC (IF‐SMPC)
Tito L. M. Santos, Sorin Olaru
wiley +1 more source
Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
Advanced Science, Volume 13, Issue 56, 9 October 2026.FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao +9 more
wiley +1 more source
Performance of LQR (Case I) under mass variation.
, 2022 Performance of LQR (Case I) under mass variation.
Jamshed Iqbal (82302) +4 more
core +1 more source
Policy Gradient for LQR with Domain Randomization
2025 IEEE 64th Conference on Decision and Control (CDC)Domain randomization (DR) enables sim-to-real transfer by training controllers on a distribution of simulated environments, with the goal of achieving robust performance in the real world. Although DR is widely used in practice and is often solved using simple policy gradient (PG) methods, understanding of its theoretical guarantees remains limited ...
Tesshu Fujinami +3 more
openaire +2 more sources
IEEE AccessThis paper presents the robust linear quadratic regulator (LQR) control for the uncertain modular multilevel converters (MMCs). The classical LQR control prioritizes optimizing the performance index but lacks the ability to address converter ...
Homa Sheikhi Jouybary +3 more
doaj +1 more source
Fractal and FractionalRecent research on fractional-order control laws has introduced the fractional calculus concept into the field of control engineering. As described herein, we apply fractional-order linear quadratic regulator (LQR) control to a current-controlled ...
Ryo Yoneda +3 more
doaj +1 more source
Deep Reinforcement Learning with embedded LQR Controllers
IFAC-PapersOnLine, 2020 This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC ...
openaire +3 more sources
Optimised output feedback PID controller for LQR inability
Linear–quadratic regulator (LQR) is a control technique successfully implemented for several complex systems. However, LQR technique in certain cases fails due to disturbances mainly caused by the improper choice of weighting matrices Q and R.
Elizabeth Rita Samuel (18105549) +2 more
core +1 more source