Nonlinear system identification and control using state transition algorithm

Abstract

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance.

20 pages, 18 figures

References in corpus (3)

• State Transition Algorithm
• Initial Version of State Transition Algorithm
• A new transformation into State Transition Algorithm for finding the global minimum

Cited by in corpus (8)

• A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm
• Discrete State Transition Algorithm for Unconstrained Integer Optimization Problems
• A dynamic state transition algorithm with application to sensor network localization
• Global solutions to a class of CEC benchmark constrained optimization problems
• A matlab toolbox for continuous state transition algorithm
• A Comparative Study of STA on Large Scale Global Optimization
• A Rolling PID Control Approach and its Applications
• Multiagent based state transition algorithm for global optimization