Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF Delta Parallel Robot for Accurate Motion Controller Design

2021 ◽  
Author(s):  
Saeed Rahimi ◽  
Hasan Jalali ◽  
Mohammad Reza Hairi Yazdi ◽  
Ahmad Kalhor ◽  
Mehdi Tale Masouleh
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Controller Design ◽  
Parallel Robot ◽  
Motion Controller

Embedded motion controller design based on RTEX network

2013 ◽  
Vol 33 (12) ◽  
pp. 3604-3607
Author(s):  
Shiyao LIN ◽  
Chongyang WU ◽  
Ruifeng LI
Keyword(s):  
Controller Design ◽  
Motion Controller

scholarly journals The Motion Controller Based on Neural Network S-plane Model for Fixed-Wing UAVs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Pengyun Chen ◽  
Guobing Zhang ◽  
Tong Guan ◽  
Meini Yuan ◽  
Jian Shen
Keyword(s):  
Neural Network ◽  
Motion Controller ◽  
Plane Model

scholarly journals Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

Neural Networks L2-Gain Controller Design for Nonlinear System

2011 ◽  
Vol 467-469 ◽  
pp. 1505-1510
Author(s):  
Dan Liu ◽  
Ni Hong Wang ◽  
Gui Ying Li
Keyword(s):  
Neural Network ◽  
Disturbance Rejection ◽  
Differential Inequality ◽  
Controller Design ◽  
Design Method ◽  
General Structure ◽  
The Neural Network ◽  
Network Weight ◽  
Finite Gain ◽  
L2 Gain

This paper proposes a new method that it uses the neural network to construct the solution of the Hamiltion-Jacobi inequality (HJ), and it carries on the optimization of the neural network weight using the genetic algorithm. This method causes the Lyapunov function to satisfy the HJ, avoides solving the HJ parital differential inequality, and overcomes the difficulty which the HJ parital differential inequality analysis. Beside this, it proposes a design method of a nonlinear state feedback L2-gain disturbance rejection controller based on HJ, and introduces general structure of L2-gain disturbance rejection controller in the form of neural network. The simulation demonstrates the design of controller is feasible and the closed-loop system ensures a finite gain between the disturbance and the output.

OPTIMAL SELF TUNING NEURAL NETWORK CONTROLLER DESIGN

2005 ◽  
Vol 38 (1) ◽  
pp. 384-389 ◽  
Author(s):  
Ladislav Körösi ◽  
Štefan Kozák
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Neural Network Controller ◽  
Network Controller ◽  
Self Tuning
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