On Robustness Of Data-Driven Model Free Adaptive Control And Learning Control

This dissertation focuses on the robustness of model free adaptive control (MFAC) and iterative learning control (ILC), which are two typical data-driven control algorithms. The unmodeled dynamic is an important aspect for the robustness of model-based control theory. However, there is no unmodeled dynamics problem for the data-driven control algorithm, because the data-driven controller is designed only using the I/O data of the controlled plant, and doesn't include any system model information. Hence, this dissertation studies the robustness of data-driven control algorithm in disturbance aspects and data dropout aspects. The main contents and key innovations are summarized as follows:1. The robustness of CFDL-MFAC algorithm with measurement disturbances and load disturbances is considered. The robust stability is given in the theoretical aspect, and the influence of the disturbances is analyzed by statistical analysis approach. The relationship between output error and disturbances statistical properties is also investigated to illustrate the influence.2. Aiming to suppress the influence of disturbances, four modified MFAC algorithms are proposed. They are the MFAC algorithm with a decreasing gain, the MFAC algorithm with a filter, the MFAC algorithm with a control input deadzone and the MFAC algorithm with disturbance observer. The convergences of modified MFAC algorithms are given, and the effectiveness and superiority of the modified algorithms are verified by simulations.3. The robustness of CFDL-MFAC algorithm with data dropout is considered. The stability of such a MFAC scheme is analyzed by the statistical approach. To evaluate the effect of data dropout, the convergent speed factor which describes the convergence speed of the MFAC process is introduced. It is shown that the output error convergent speed gets slower as dropout rate increases. The analysis is supported by simulations.4. Aiming to suppress the influence of data dropout, a MFAC algorithm with data compensation is proposed. The convergence analysis of the modified MFAC algorithm is given, and the effectiveness is supported by simulations.5. The robustness of iterative learning control algorithm with data dropout is considered. Using the so-called super-vector approach to ILC, the first order ILC scheme and the high order ILC scheme are both considered, and the expectation of output error is employed to develop the condition for stability of such the ILC process. Furthermore, the Hχiterative learning controller is designed when the discrete-time systems subject to both data dropout and iteration varying disturbance, which can guarantee both stability and the desired H∞performance in the iteration domain. The analysis is supported by numerical examples.