Online adaptation of controller parameters based on approximate dynamic programming

Guo, Wentao; Liu, Feng; Si, Jennie; Mei, Shengwei

doi:10.1109/ijcnn.2014.6889869

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…A EAR pode ser vista como uma forma particular da equação HJB associada ao problema do regulador linear quadrático (RLQ) discreto. Segundo [43], a abordagem RMQ é um treinamento eficiente de segunda ordem que leva a uma convergência mais rápida em comparação com as abordagens de primeira ordem, tais como o algoritmo de retro propagação (BP -backpropagation) e algoritmo do gradiente descendente, comumente usados em aprendizado de redes neurais [44,45].…”

Section: Introductionunclassified

Optimal Control and Adaptive Learning for Stabilization of a Quadrotor-type Unmanned Aerial Vehicle via Approximate Dynamic Programming

Souza

Rêgo

Sousa

et al. 2022

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The development of an optimal controller for stabilization of a quadrotor system using an adaptive critic structure based on policy iteration schemes is proposed in this paper. This approach is inserted in the context of Approximate Dynamic Programming and it is used to solve optimal decision problems on-line, without requiring complete knowledge of the system dynamics model to be controlled. The main feature of the adaptive critic design method that allows for on-line implementation is that it solves the Bellman optimality equation in a forward-in-time fashion, whereas traditional dynamic programming requires a backward-in-time procedure. This feedback control design technique is able to tune the controller parameters on-line in the presence of variations in plant dynamics and external disturbances using data measured along the system trajectories. Computational simulation results based on a quadrotor model demonstrate the effectiveness of the proposed control scheme.

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Section: Introductionunclassified