Loading...

A non-linear estimation and model predictive control algorithm based on ant colony optimization

Nobahari, H ; Sharif University of Technology | 2019

779 Viewed
  1. Type of Document: Article
  2. DOI: 10.1177/0142331218798680
  3. Publisher: SAGE Publications Ltd , 2019
  4. Abstract:
  5. A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar scheme to the optimization algorithm, known as the continuous ant colony system. Performance of the new model predictive controller is evaluated for three non-linear problems. The problems are a non-linear continuous stirred tank reactor, a non-linear cart and spring system, and the attitude control of a non-linear quadrotor. The results verify successful performance of the proposed algorithm from both estimation and control points of view. © The Author(s) 2018
  6. Keywords:
  7. Ant colony optimization ; Multi-input multi-output control ; Non-linear stochastic system ; Artificial intelligence ; Attitude control ; Cost functions ; MIMO systems ; Model predictive control ; Predictive control systems ; State estimation ; Stochastic control systems ; Stochastic models ; Stochastic systems ; Augmented state space ; Continuous stirred tank reactor ; Heuristic control ; Model predictive controllers ; Multi-input multi-output controls ; Non-linear estimation ; Non-linear stochastic systems ; Optimization algorithms ; Controllers
  8. Source: Transactions of the Institute of Measurement and Control ; Volume 41, Issue 4 , 2019 , Pages 1123-1138 ; 01423312 (ISSN)
  9. URL: https://journals.sagepub.com/doi/abs/10.1177/0142331218798680