Study On Model Free Adaptive Control For Nonlinear Systems

In recent years, with the rapid development of information science and technology, the scale of industrial production has been expanded rapidly, as well as that practical production processes have become more complex. Therefore, model based control theory has faced enormous difficulty in achieving expected effect. As a result, data driven model-free adaptive control theory emerges at the right moment. With development of more than 20 years, theoretical analysis framework has been established for model-free adaptive control theory, basically. However, model-free adaptive control theory is at primary stage, and there are still many problems needed further study.This dissertation focus on the study of model-free adaptive control theory for nonlinear systems. The main work is summarized as following: Firstly, the problem of trajectory tracking for SISO and MIMO nonlinearFirstly, the problem of trajectory tracking for SISO and MIMO nonlinear discrete-time systems subject to external disturbance is concerned. A novel dynamic linearization method is proposed for the system with unknown disturbance. The robust stability of the existing model-free adaptive control system is given in theoretical aspect, and the influence of external disturbance is analyzed, which illustrates the relationship between tracking error and external disturbance.Secondly, aiming to suppress the influence of disturbance for SISO and MIMO nonlinear discrete-time system, a novel disturbance observer is proposed based on nominal equivalent data model to estimate the equivalent disturbance firstly. Then a modified model-free adaptive control algorithm is proposed. Finally, the convergence of system is analyzed, and the effectiveness and superiority of the modified algorithm are verified by simulations.Thirdly, the problem of tracking time-varying trajectory for the SISO and MIMO nonlinear discrete-time systems is concerned. The disturbance observer based on the equivalent data model is established to estimate and compensate the estimation error of the data model. In addition, a modified model-free adaptive algorithm is proposed, and the rigorous mathematic proofs show that the system not only can track desired slow time-varying trajectory, but also can guarantee that the tracking error of the system converges to zero. Simulation is presented to verify the effectiveness of the proposed method.