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

Author(s):  
Linh Nguyen

<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>

2021 ◽  
Author(s):  
Linh Nguyen

<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>


Author(s):  
Paolo Roberto Massenio ◽  
David Naso ◽  
Gianluca Rizzello

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.


2013 ◽  
Vol 336-338 ◽  
pp. 852-855
Author(s):  
Bao Sheng Yang ◽  
Bao Sheng Yang ◽  
Li Li Chen

Time delays are tremendous difficulties to system stability analysis and controller design, and often cause system instability or even lead to the deterioration of the system performance. The optimal control law is solved for a class of nonlinear discrete affine systems with multiple time delays. The systems HJB equation of optimal control is solved by adaptive dynamic iterative algorithm and minimized system performance index function, given the convergence proof of the algorithm. Neural networks are adopted to realize iterative algorithm for optimal control law of time delay systems. The simulation results show that the adaptive dynamic programming can solve for the optimal control of delay nonlinear systems, make the system to achieve stability.


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