This paper develops multiple fixed model blending based adaptive parameter identification schemes for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification schemes are proven to be asymptotically stable for uncertain linear time-invariant (LTI) MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification schemes, a linear-quadratic (LQ) optimal multiple-model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is proven to be asymptotically stable for LTI MIMO systems and applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability, effectiveness, and comparison of the proposed MMAC scheme.

本文针对具有多参数不确定性的多输入多输出（MIMO）系统，开发了基于多种固定模型混合的自适应参数识别方案。事实证明，所开发的识别方案对于不确定的线性时不变（LTI）MIMO系统是渐近稳定的，并且被证明可为不确定的线性时变（LTV）系统提供快速适应性。此外，利用提出的参数识别方案，针对具有多主题不确定性的线性MIMO系统，开发了线性二次（LQ）最优多模型自适应控制（MMAC）方案。事实证明，所提出的MMAC方案对于LTI MIMO系统是渐近稳定的，并应用于不确定的横向车辆动力学的跟踪控制。提出了一组模拟测试结果以验证稳定性，