scholarly journals Adaptive dynamic programming enhanced admittance control for robots with environment interaction and actuator saturation

2021 ◽  
Vol 5 (1) ◽  
pp. 89-100
Author(s):  
Hong Zhan ◽  
Dianye Huang ◽  
Chenguang Yang
Keyword(s):  
Dynamic Programming ◽  
Actuator Saturation ◽  
Adaptive Dynamic Programming ◽  
System Stability ◽  
Environment Interaction ◽  
Adaptive Dynamic ◽  
Control Scheme ◽  
Robot Systems ◽  
Hamilton Jacobi Bellman ◽  
The Cost

AbstractThis paper focuses on the optimal tracking control problem for robot systems with environment interaction and actuator saturation. A control scheme combined with admittance adaptation and adaptive dynamic programming (ADP) is developed. The unknown environment is modelled as a linear system and admittance controller is derived to achieve compliant behaviour of the robot. In the ADP framework, the cost function is defined with non-quadratic form and the critic network is designed with radial basis function neural network which introduces to obtain an approximate optimal control of the Hamilton–Jacobi–Bellman equation, which guarantees the optimal trajectory tracking. The system stability is analysed by Lyapunov theorem and simulations demonstrate the effectiveness of the proposed strategy.

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.

scholarly journals Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092461
Author(s):  
Hong Zhan ◽  
Dianye Huang ◽  
Zhaopeng Chen ◽  
Min Wang ◽  
Chenguang Yang
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Linear Quadratic Regulator ◽  
Adaptive Dynamic Programming ◽  
Environment Interaction ◽  
Linear Quadratic ◽  
Adaptive Dynamic ◽  
The Neural Network ◽  
Network Weight ◽  
The Stability

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee the compliant behaviour. Meanwhile, an adaptive dynamic programming-based controller is proposed. Under adaptive dynamic programming frame, the critic network is performed with radial basis function neural network to approximate the optimal cost, and the neural network weight updating law is incorporated with an additional stabilizing term to eliminate the requirement for the initial admissible control. The stability of the system is proved by Lyapunov theorem. The simulation results demonstrate the effectiveness of the proposed control scheme.

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