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
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Using longitudinal qualitative research to understand the experiences of minoritized people
Methods in PsychologyThis paper demonstrates how Longitudinal Qualitative Research (LQR) is an innovative method to understand the lived experiences of members of minoritized groups when temporality is a structuring element of their experiences.
Christine V. Wood +5 more
doaj +1 more source
Hierarchical Reinforcement Learning–Based Optimal Control for Model-Free Linear Systems
MathematicsA novel model-free hierarchical reinforcement learning (HRL)–based Linear Quadratic Regulator (LQR) control framework with adaptive weight selection is proposed to address the reliance of conventional LQR methods on accurate system models and manual ...
Yong Zhang +3 more
doaj +1 more source
Weighting Matrix Selection Method for LQR Design Based on a Multi-Objective Evolutionary Algorithm
, 2011 This paper introduces an application of Multi-Objective Evolution Algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR).
Mohammad Ali Nekoui +1 more
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Adaptive Weight Generation Neural Network LQR Control for Energy-Regenerative Suspension
MachinesVehicle energy-regenerative suspension can convert part of the vibration energy induced by road excitation into electrical energy. However, there are coupled performance conflicts among energy recovery, ride comfort, and suspension safety, and a fixed ...
Buyun Zhang +4 more
doaj +1 more source
On Globally Optimal Stochastic Policy Gradient Methods for Domain Randomized LQR Synthesis
CoRRDomain randomization is a simple, effective, and flexible scheme for obtaining robust feedback policies aimed at reducing the sim-to-real gap due to model mismatch. While domain randomization methods have yielded impressive demonstrations in the robotics-learning literature, general and theoretically motivated principles for designing optimization ...
Alex Nguyen-Le, Nikolai Matni
openaire +3 more sources
Convergence Guarantees of Model-free Policy Gradient Methods for LQR with Stochastic Data
CoRRPolicy gradient (PG) methods are the backbone of many reinforcement learning algorithms due to their good performance in policy optimization problems. As a gradient-based approach, PG methods typically rely on knowledge of the system dynamics. If this is not available, trajectory data can be utilized to approximate first-order information.
Bowen Song, Andrea Iannelli
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An efficient algorithm for solving linear equality-constrained LQR problems [PDF]
We present a new algorithm for solving linear-quadratic regulator (LQR) problems with linear equality constraints, also known as constrained LQR (CLQR) problems.
Orban, Dominique, Sousa-Pinto, João
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Policy Gradient Methods for the Cost-Constrained LQR: Strong Duality and Global Convergence
IEEE Transactions on Automatic ControlIn safety-critical applications, reinforcement learning (RL) needs to consider safety constraints. However, theoretical understandings of constrained RL for continuous control are largely absent. As a case study, this paper presents a cost-constrained LQR formulation, where a number of LQR costs with user-defined penalty matrices are subject to ...
Feiran Zhao, Keyou You
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Vehicle lateral tracking control optimization based on fuzzy preview time and ant lion algorithm
Frontiers in Mechanical EngineeringTo enhance the path tracking performance of intelligent vehicles, this paper conducts optimization research on the classical Linear Quadratic Regulator (LQR) controller based on a 2-degrees-of-freedom (2-DOF) vehicle dynamics lateral tracking error model.
Liang Yanjie +4 more
doaj +1 more source