Neural Network Tracking Control of Unknown Servo System with Approximate Dynamic Programming

2019 ◽  
Author(s):  
Yongfeng Lv ◽  
Xuemei Ren ◽  
Tianyi Zeng ◽  
Linwei Li ◽  
Jing Na
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Tracking Control ◽  
Approximate Dynamic Programming ◽  
Servo System

Neural Network Identification and Direct Adaptive Fuzzy Neural Network (DAFNN) Controller for Unknown Nonlinear Non-affine Pneumatic Servo System

2021 ◽  
Author(s):  
Peyman Mawlani ◽  
Mohammadreza Arbabtafti
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Neural Network ◽  
Servo System ◽  
Trajectory Tracking Control ◽  
Adaptive Fuzzy Neural Network ◽  
Non Linear ◽  
Fuzzy Neural ◽  
Pneumatic Servo System

In this paper, a direct adaptive fuzzy neural network (DAFNN) controller for trajectory tracking control of the non-linear non-affine pneumatic servo system is presented. First, using a neural network identifier, the non-linear dynamics of a real pneumatic servo system is simulated. By comparing the output of the neural network and the output of the experimental setup, it is observed that the non-linear pneumatic actuator system is well-identified using neural networks. By incorporating the Lyapunov stability theorem, the adaptive laws for the parameters of the controller are obtained, parameter boundedness and stability of the closed-loop system are guaranteed. Finally, practical results are successfully implemented for trajectory tracking control of the pneumatic servo system, in which the effect of the simultaneous updating of the antecedent and consequent’s parameters of the fuzzy neural network controller has been investigated. The tracking error ±1.3mm and ±1 mm for proposed updating method compared to ±2.5mm and ±3.5mm, for a case that the weigh parameters are merely adjusted, are obtained. The results indicate the proposed adjustment method improves the performance of the controller in the presence of unknown nonlinearities and dynamics uncertainty.

Approximate Dynamic Programming in the Sensor-Based Navigation of the Wheeled Mobile Robot

2015 ◽  
Vol 220-221 ◽  
pp. 60-66 ◽  
Author(s):  
Zenon Hendzel ◽  
Marcin Szuster
Keyword(s):  
Dynamic Programming ◽  
Control System ◽  
Path Planning ◽  
Mobile Robot ◽  
Tracking Control ◽  
Approximate Dynamic Programming ◽  
Wheeled Mobile Robot ◽  
Discrete Control ◽  
Heuristic Dynamic Programming ◽  
Control Layer

The article presents a new approach to the sensor-based navigation of wheeled mobile robot Pioneer 2-DX in the unknown 2-D environment with static obstacles. The navigation task has been developed using a discrete hierarchical control system with a path planning layer and a tracking control layer designed using approximate dynamic programming algorithms. The navigator realises a behavioural control approach to the path planning process using the adaptive coordination of two simple behaviours: “goal-seeking” and “obstacle avoiding”. The main part of the navigator is the Action-Dependant Heuristic Dynamic Programming structure realised in a form of the actor and critic neural networks. To avoid the time consuming trial and error learning, additional proportional controllers generating signals that prompt the direction of the sub-optimal control law seeking process at the beginning of the NNs adaptation process are arranged in the navigator. The tracking control layer is composed of a PD controller, the Dual Heuristic Dynamic Programming algorithm and a supervisory term. It generates control signal for DC motors of the robot. The performance of the proposed discrete control system was verified by a series of experiments conducted using wheeled mobile robot Pioneer 2-DX equipped with one laser and eight ultrasonic range finders that provide object detection.

scholarly journals Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

2021 ◽  
Author(s):  
Xiaolin Ren ◽  
Hongwen Li
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Cost Function ◽  
Tracking Control ◽  
Feature Tracking ◽  
Visual Servoing ◽  
Error Control ◽  
Lyapunov Theory ◽  
Adaptive Dynamic Programming ◽  
Adaptive Dynamic

AbstractThis paper investigates a feature tracking control method for visual servoing (VS) manipulators adaptive dynamic programming (ADP)-based the unknown dynamics. The major superiority of ADP-based optimal control lies in that the visual tracking problem is converted to the feature tracking error control with optimal cost function. Moreover, an adaptive neural network observer is developed to approximate the entire uncertainties, which are utilized to construct an improved cost function. By establishing a critic neural network, the Hamilton–Jacobi–Bellman (HJB) equation is solved, and the approximate optimal error control policy is derived. The closed-loop VS manipulator system is verified to be ultimately uniformly bounded with the developed ADP-based feature tracking control strategy according to the Lyapunov theory. Finally, simulation results under various situations demonstrate that the proposed method achieves higher tracking accuracy than other methods, as well as satisfies energy optimal requirements.

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