Total views : 508

Jib System Control of Industrial Robotic Three Degree of Freedom Crane using a Hybrid Controller

Affiliations

  • School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), H-12 Main Campus, Islamabad, Pakistan

Abstract


Background/Objectives: Cranes are used to carry loads effectively. During movement, often undesired fluctuations of lifted payload occur, which needs to be controlled. Control is the basic requirement for desired operation of crane. Objective is to control the trolley position and swing angle of payload. Methods/Statistical Analysis: The continual flow requires an effective control methodology to achieve a high positioning control of the trolley carrying payload and suppression of swing angle of payload during operation. Optimal control techniques can be used to control these undesired vibrations. These techniques result in some undesired overshoot and undershoot causing the payload to swing prior to system getting stable. However if these techniques are combined with intelligent control techniques then a more stable system can be obtained. Findings: In this paper a hybrid controller called neuro-optimal controller has been used to control the swing angle of lifted payload by controlling the trolley position.The proposed technique of using a hybrid controller has stabilized the system by reducing the overshoot, undershoot and settling time. Application/Improvements: The proposed technique is very useful in many industrial applications. Experimental analysis can further provide the insight and limitations of the proposed techniques.

Keywords

Artificial Neural Network (ANN), Algebraic Riccati Equation (ARE), Back Propagation (BP), Linear Quadratic Regulator Controller (LQR), Neural Network Predictive Controller (NNPC), 3 Degree of Freedom (3DOF).

Full Text:

 |  (PDF views: 352)

References


  • Jalani J. Anti-swing control strategy for automatic 3 DOF crane system using FLC. Indian Journal of Mechanical Engineering. 2000; 1(6):1–9.
  • Faisalm M, Jamil Q , Awais U, Rashid MSSO, Gilani Y, Ayaz A. Iterative Linear Quadratic Regulator (ILQR) controller for trolley position control of quanser 3DOF Crane. Indian Journal of Science and Technology. 2015; 8(16):1–7.
  • Faisal M, Jamil M, Rashid U, Gilani SO, Ayaz Y, Khan MN. A novel dual-loop control scheme for payload anti-swing and trolley position of industrial robotic 3DOF crane. Applied Mechanics and Materials. 2015; 658–64.
  • Jovanovic Z, Antic D, Stajic Z, Milosevic M, Nikolic S, Peric S. Genetic algorithms applied in parameters determination of the 3D crane model. Facta Universitatis, Series: Automatic Control and Robotics. 2011; 10:19–27.
  • Jamil M, Janjua AA, Rafique I, Butt SI, Ayaz Y, Gilani SO. Optimal control based intelligent controller for active suspension system. Life Science Journal. 2013; 10(12s):653–9.
  • Abdel-Rahman EM, Nayfeh AH, Masoud ZN. Dynamics and control of cranes: A review. Journal of Vibration and Control. 2003; 9:863–908.
  • Murthy BV, Kumar YVP, Kumari UVR. Application of neural networks in process control: Automatic/online tuning of PID controller gains for 10% disturbance rejection. IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT); 2012. p. 348–52.
  • Chopra V, Singla SK, Dewan L. Comparative analysis of tuning a PID controller using intelligent methods. Acta Polytechnica Hungarica. 2014; 11(1):235–48.
  • Chen H, Gao B, Zhang X. Dynamical modelling and nonlinear control of a 3d crane. International Conference on Control and Automation, ICCA’05; 2005. p. 1085–90.
  • Vikramaditya B, Rajamani R. Nonlinear control of a trolley crane system. Proceedings of the American Control Conference; 2000. p. 1032–6.
  • Yang JH, Yang KS. Adaptive coupling control for overhead crane systems. Mechatronics. 2007; 17(1):143–52.
  • Luoren L, Jinling L. Research of PID control algorithm based on neural network. Energy Procedia. 2011; 13:6988– 93.
  • Rekik C, Djemel M, Derbel N. On the neuro-genetic approach for determining optimal control of a rotary crane. Proceedings of 2003 IEEE Conference on Control Applications, CCA 2003; 2003. p. 124–8.
  • Khwaja S, Jamil S, Awais Q, Asghar U, Ayaz Y. Analysis of classical controller by variation of inner loop and controller gain for two level grid connected converter. Indian Journal of Science and Technology. 2015; 8(20):1–5.
  • Available from: www.quanser.com/products/3dof_crane
  • Demuth H, Beale M. Neural network toolbox for use with MATLAB; 1993.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.