This paper develops a new model reference adaptive control (MRAC) framework using partial-state feedback for solving a multivariable adaptive output tracking problem. The developed MRAC scheme has full capability to deal with plant uncertainties for output tracking and has desired flexibility to combine the advantages of full-state feedback MRAC and output feedback MRAC. With such a new control scheme, the plant-model matching condition is achievable as with an output or state feedback MRAC design. A stable adaptive control scheme is developed based on LDS decomposition of the plant high-frequency gain matrix, which guarantees closed-loop stability and asymptotic output tracking. The proposed partial-state feedback MRAC scheme not only expands the existing family of MRAC, but also provides new features to the adaptive control system, including additional design flexibility and feedback capacity. Based on its additional design flexibility, a minimal-order MRAC scheme is also presented, which reduces the control adaptation complexity and relaxes the feedback information requirement, compared to the existing MRAC schemes. New results are presented for plant-model matching, error model, adaptive law and stability analysis. A simulation study of a linearized aircraft model is conducted to demonstrate the effectiveness and new features of the proposed MRAC control scheme.
Three Laws of Multivariable Feedback Systems, Extended Spectral Flatness (Extended Wiener Entropy), 'Uncertainty Principles' in Variance Minimization, and Performance Limitations in Minimum Variance Estimation/Filtering
Cerebellar-Inspired Learning Rule for Gain Adaptation of Feedback Controllers
SPECS Lab, University Pompeu Fabra（西班牙庞培法布拉大学 SPECS 实验室）；Research Center “Enrico Piaggio”, University of Pisa（意大利比萨大学恩里科·皮吉奥研究中心）；Department of Advanced Robotics, Istituto Italiano di Tecnologia（意大利理工学院先进机器人系）；Institució Catalana de Recerca i Estudis Avançats（加泰罗尼亚高级研究学院）
