Intelligent control of industrial robotic three degree of freedom crane using Artificial Neural Network

Cranes are widely used all over the world in industrial sector to reduce the man hours and increase efficiency. It is used for carrying load from one place to another, during this movement there are often undesired vibrations and fluctuations of lifted payload which needs to be controlled. Conventional PID techniques such as tuning of PID gains through Linear Quadratic Regulator (LQR) controller can be used to control these undesired vibrations. These techniques also result in some undesired overshoot and undershoot causing the payload to swing prior to system getting stable. However if these gains are further tuned by Artificial Neural Networks (ANN) then this undesired overshoot and undershoot can also be controlled.

[1]  Yang Jung Hua,et al.  Adaptive coupling control for overhead crane systems , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[2]  Yasar Ayaz,et al.  Optimal Control based Intelligent Controller for Active Suspension System , 2013 .

[3]  Ruslinda Ruslee.  Anti - swing control strategy for automatic 3 DOR crane system using FLC , 2008 .

[4]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[5]  Yasar Ayaz,et al.  A Novel Dual-Loop Control Scheme for Payload Anti-Swing and Trolley Position of Industrial Robotic 3DOF Crane , 2015 .

[6]  Ali H. Nayfeh,et al.  Dynamics and Control of Cranes: A Review , 2003 .

[7]  Y. V. Pavan Kumar,et al.  Application of neural networks in process control: Automatic/online tuning of PID controller gains for ±10% disturbance rejection , 2012, 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).

[8]  Liu Luoren,et al.  Research of PID Control Algorithm Based on Neural Network , 2011 .

[9]  Hongjun Chen,et al.  Dynamical modelling and nonlinear control of a 3D crane , 2005, 2005 International Conference on Control and Automation.

[10]  Rajesh Rajamani,et al.  Nonlinear control of a trolley crane system , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[12]  Y. V. Pavan Kumar,et al.  Application of neural networks in process control: Automatic/online tuning of PID controller gains for ±10% disturbance rejection , 2012 .