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Volume 2 Issue 1
Jan.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2015  >  2(1): 85-93
Zhou Fang, Hua Tian and Ping Li, "Probabilistic Robust Linear Parameter-varying Control of a Small Helicopter Using Iterative Scenario Approach," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 85-93, 2015.
Citation: Zhou Fang, Hua Tian and Ping Li, "Probabilistic Robust Linear Parameter-varying Control of a Small Helicopter Using Iterative Scenario Approach," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 85-93, 2015.
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Probabilistic Robust Linear Parameter-varying Control of a Small Helicopter Using Iterative Scenario Approach

  • 1. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China;

  • 2. Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;

  • 3. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China and Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Funds:

This work was supported by National Natural Science Foundation of China (61004066).

    Abstract
  • In this paper, we present an iterative scenario approach (ISA) to design robust controllers for complex linear parameter-varying (LPV) systems with uncertainties. The robust controller synthesis problem is transformed to a scenario design problem, with the scenarios generated by identically extracting random samples on both uncertainty parameters and scheduling parameters. An iterative scheme based on the maximum volume ellipsoid cutting-plane method is used to solve the problem. Heuristic logic based on relevance ratio ranking is used to prune the redundant constraints, and thus, to improve the numerical stability of the algorithm. And further, a batching technique is presented to remarkably enhance the computational efficiency. The proposed method is applied to design an output-feedback controller for a small helicopter. Multiple uncertain physical parameters are considered, and simulation studies show that the closed-loop performance is quite good in both aspects of model tracking and dynamic decoupling. For robust LPV control problems, the proposed method is more computationally efficient than the popular stochastic ellipsoid methods.

     

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