Results 71 to 80 of about 16,024 (227)
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
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
openaire +3 more sources
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
openaire +3 more sources
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
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
Constrained LQR using online decomposition techniques [PDF]
2016 IEEE 55th Conference on Decision and Control (CDC), 2016 This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints.
Laura Ferranti +3 more
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International Insolvency Review, EarlyView.Abstract This paper conducts a comparative legal analysis of corporate restructuring frameworks in England and Bhutan, examining their capacity to integrate climate variability considerations and promote sustainable business practices. It discusses the procedural mechanisms for restructuring financially distressed enterprises available under the law of
Eugenio Vaccari, Migmar Lham
wiley +1 more source
Journal of the Franklin Institute, 2020 In this paper, a Fuzzy based Linear Quadratic Regulator (FLQR) and Linear Quadratic Gaussian (FLQG) controllers are developed for stability control of a Double Link Rotary Inverted Pendulum (DLRIP) system.
Zied Ben Hazem, M. Fotuhi, Z. Bingul
semanticscholar +1 more source
A Singularity-Robust LQR Controller for Parallel Robots [PDF]
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 Trabajo presentado en la IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), celebrada en Madrid, del 1 al 5 de octubre de ...
Bordalba Llaberia, Ricard +2 more
openaire +3 more sources
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