State-varying optimal decoupled sliding mode control for the Lorenz chaotic nonlinear problem based on HEPSO and MLS

ABSTRACT

In order to gain the optimal performance of a controller, the proper selection of its parameters is of great importance. Moreover, any changes in the values of the parameters of the system cause that the respected controller works in a non-optimal status. Hence, to prevail over these obstacles, a state-varying optimal Decoupled Sliding Mode Control (DSMC) method is proposed in this research. First, the High Exploration Particle Swarm Optimization (HEPSO) approach is employed to find the optimal parameters of the DSMC. Then, the Moving Least Squares (MLS) approximation method is used to adjust the optimal gains of the controller according to the new parameters of the system. Lastly, the proposed state-varying optimal DSMC is utilized to address the Lorenz chaotic problem. The efficacy of the proposed controller is illustrated via comparing its performance with other notable studies.

KEYWORDS:

• Decoupled sliding mode control
• state-varying optimal control
• high exploration particle swarm optimization
• moving least squares
• Lorenz chaotic problem

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Mohammad Javad Mahmoodabadi

Mohammad Javad Mahmoodabadi received his BSc and MSc degrees in Mechanical Engineering from Shahid Bahonar University of Kerman, Iranin 2005 and 2007, respectively. He received his Ph.D. degree in Mechanical Engineering from the University of Guilan, Rasht, Iran in 2012. He worked for 2 years in the Iranian textile industries. During his research, he was a scholar visitor with Robotics and Mechatronics Group, University of Twente, Enchede, the Netherlands for 6 months. Now, he is an Assistant Professor of Mechanical Engineering at the Sirjan University of Technology, Sirjan, Iran. He has published about 100 scientific articles in international journals and conference proceedings. His research interests include optimization algorithms, non- linear and robust control, and computational methods.