Direct Adaptive Fuzzy Control For Uncertain Nonlinear Systems

With the development of science and technology, the process of industrial production has become complicated. Therefore, influenced by uncertainty, strong nonlinearity and multiple variables, it is difficult to describe the dynamic characteristics of the system with an exact mathematical model. Fuzzy control provides an effective way to solve the problem of modeling and control for complex nonlinear systems. According to the backstepping technique and the universal approximation ability of fuzzy logic systems, this thesis mainly considers the uncertain nonlinear systems with unmodeled dynamics. Two kinds of direct adaptive fuzzy control methods have been proposed for uncertain nonlinear strict-feedback systems with un-measurable states. The main contents are given as follows:1. Background and current research of adaptive fuzzy control and backstepping technique are introduced.2. Notations and fuzzy logic system are proposed.3. A direct adaptive fuzzy tracking control method is proposed for a class of single-input single-output (SISO) strict-feedback uncertain systems with unmodeled dynamics. Under the condition that the states are un-measurable, system and assumptions become more general. The proposed fuzzy adaptive controller not only guarantees the closed-loop system being input-to-state practically stable (ISpS), but also achieves semi-globally uniformly ultimately bounded parameter signals. At the same time, the tracking error converges to a small neighborhood of the origin. In order to reduce the computation burden of the system control, only three adaptive parameters need to be estimated online, consequently increasing the operating efficiency of the system. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed method.4. A direct adaptive fuzzy controller is constructively designed for a class of SISO uncertain systems with stochastic disturbance. The stochastic nonlinear systems possess dynamic disturbances in the presence of unstructured uncertainties and unmodeled dynamics. The assumptions on the unmodeled dynamics are more general. It is proved that the closed-loop system is ISpS in probability by using the backstepping technique and stochastic small-gain approach. Moreover, the proposed control algorithm only contains four parameters that need to be updated online,which makes parameter adaptive laws simpler and significantly reduces the computation burden and improves the control efficiency of the system. Note that fuzzy logic systems are directly used to approximate unknown control signals.Therefore,the proposed approach can effectively solve the problem of "explosion of complexity". Finally, the effectiveness of the proposed control approach is demonstrated by two simulation examples.5. The research contents and results are summarized, and the future research direction is prospected.