A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 4
Oct.  2014

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
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Article Contents
Nhan T. Nguyen and Sivasubramanya N. Balakrishnan, "Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 423-434, 2014.
Citation: Nhan T. Nguyen and Sivasubramanya N. Balakrishnan, "Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 423-434, 2014.

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

  • This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and the predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and the predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

     

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