Results 61 to 70 of about 12,042 (216)
Policy Evaluation in Distributional LQR
IEEE Transactions on Automatic ControlDistributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL. At the same time, a main challenge in DRL is that policy evaluation in DRL typically relies on the representation of the
Zifan Wang 0002 +5 more
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Advanced Intelligent Discovery, EarlyView.The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
On the design of LQR kernels for efficient controller learning [PDF]
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017 Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective ...
Alonso Marco +3 more
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Advanced Intelligent Systems, EarlyView.This study presents an integrated proof‐of‐concept robotic platform for targeted delivery applications. A hybrid magnetic actuation system achieves single‐input (z) multistate stabilization (x, y, ϕ, and θ) for precise capsule transport with submillimeter positioning accuracy.
Sejun Park +6 more
wiley +1 more source
Multi-objective optimization of LQR control quarter car suspension system using genetic algorithm [PDF]
FME Transactions, 2016 In this paper, genetic algorithm (GA) based multi-objective optimization technique is presented to search optimum weighting matrix parameters of linear quadratic regulator (LQR). Macpherson strut suspension system is implemented for study.
Nagarkar M.P., Patil Vikhe G.J.
doaj
Asian Journal of Control, EarlyView.Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
wiley +1 more source
Inertial‐Based LQG Control: A New Look at Inverted‐Pendulum Stabilization
International Journal of Mechanical System Dynamics, EarlyView.ABSTRACT Linear‐quadratic Gaussian (LQG) control is a well‐established method for optimal control through state estimation, particularly in stabilizing an inverted pendulum on a cart. In standard laboratory setups, sensor redundancy enables direct measurement of configuration variables using displacement sensors and rotary encoders. However, in outdoor
Daniel Engelsman, Itzik Klein
wiley +1 more source
Optimal control of cuk converter using LQR [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2023 In this paper, linear quadratic regulator (LQR) control is applied to a Cuk converter, and mathematical modeling of the converter is done using state space averaging (SSA) in continuous conduction mode (CCM). The primary focus is to design a controller
Deepak Kumar Singh +2 more
doaj
Transfer Learning for LQR Control
CoRRIn this paper, we study a transfer learning framework for Linear Quadratic Regulator (LQR) control, where (i) the dynamics of the system of interest (target system) are unknown and only a short trajectory of impulse responses from the target system is provided, and (ii) impulse responses are available from $N$ source systems with different dynamics. We
Taosha Guo, Fabio Pasqualetti
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