Nearly Optimal Control Scheme Using Adaptive Dynamic Programming Based on Generalized Fuzzy Hyperbolic Model

2013 ◽  
Vol 39 (2) ◽  
pp. 142-148 ◽  
Author(s):  
Ji-Lie ZHANG ◽  
Hua-Guang ZHANG ◽  
Yan-Hong LUO ◽  
Hong-Jing LIANG
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Adaptive Dynamic Programming ◽  
Hyperbolic Model ◽  
Adaptive Dynamic ◽  
Control Scheme ◽  
Optimal Control Scheme ◽  
Generalized Fuzzy Hyperbolic Model

scholarly journals Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Adaptive Dynamic Programming ◽  
Control Law ◽  
Parameter Uncertainties ◽  
Adaptive Dynamic ◽  
Uncertain Dynamics ◽  
Control Scheme ◽  
State Controller ◽  
Optimal Control Scheme

<div>The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown</div><div>and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic</div><div>two-wheel robot where the obtained results demonstrate its</div><div>effectiveness.</div>

scholarly journals Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Adaptive Dynamic Programming ◽  
Control Law ◽  
Parameter Uncertainties ◽  
Adaptive Dynamic ◽  
Uncertain Dynamics ◽  
Control Scheme ◽  
State Controller ◽  
Optimal Control Scheme

<div>The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown</div><div>and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic</div><div>two-wheel robot where the obtained results demonstrate its</div><div>effectiveness.</div>

Energy Optimal Control of Dielectric Elastomer Actuators via Adaptive Dynamic Programming

2019 ◽  
Author(s):  
Paolo Roberto Massenio ◽  
David Naso ◽  
Gianluca Rizzello
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Dielectric Elastomer ◽  
Adaptive Dynamic Programming ◽  
Set Point ◽  
Adaptive Dynamic ◽  
Strongly Nonlinear ◽  
Dielectric Elastomer Actuators ◽  
Control Scheme ◽  
Dielectric Elastomer Membrane

Abstract This paper presents an optimal motion control scheme for a mechatronic actuator based on a dielectric elastomer membrane transducer. The optimal control problem is formulated such that a desired position set-point is reached with minimum amount of driving energy, characterized via an accurate physical model of the device. Since the considered actuator is strongly nonlinear, an approximated approach is required to practically address the design of the control system. In this work, an Adaptive Dynamic Programming based algorithm is proposed, capable of minimizing a cost function related to the energy consumption of the considered system. Simulation results are presented in order to assess the effectiveness of the proposed method, for different set-point regulation scenarios.

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