Adaptive Fuzzy Control Of Uncertain Nonlinear Systems

The fuzzy control has been regarded as an effective way to cope with the analysis and control problem for complex nonlinear systems. The research on stability and controller design of nonlinear systems via different fuzzy models has been a focus in fuzzy control fields. Specifically, due to the approximation property of the fuzzy systems, the adaptive fuzzy control of uncertain nonlinear systems has been an active research topic in international automatic control area in recent years. With the development of correlative disciplinary, it has an important theory significance to make use of the advantage of fuzzy control to solve difficult problems which are not solved by traditional methods. Based on the fuzzy logic systems the human language information is incorporated into the control systems in this dissertation. Then, the new system analysis and design methods are proposed for the uncertain nonlinear systems which are difficult to be modeled. The main work of this dissertation is as follows.An adaptive fuzzy control approach is proposed for a class of uncertain nonlinear pure-feedback singal-input-singal-output systems. Compared with the existing results, the main advantages of this approach are that only one fuzzy system is used to approximate the system uncertainties and the robustness of the closed-loop system is improved. Based on Lyapunov stability analysis, the proposed approach guarantees that all the signals in the closed-loop system are ultimately bounded and the tracking error converges to a small neighborhood around zero. Simulation results show the effectiveness and feasibility of the proposed approach.A nonlinear multiple-input-multiple-output (MIMO) system with unknown interconnected functions is a kind of complicated systems. Because the inter-connected function appears in every equation of each subsystem and the input gain coefficients are unknown nonlinear functions, therefore it makes this kind of systems to be more difficult to control. A robust adaptive fuzzy control method is proposed in this dissertation for this kind of systems. In the controller design, the robust control terms are used to compensate the approximation errors and this improves the system robustness. Under the appropriate assumption condition, the algorithm overcomes the controller singularity problem of nonlinear MIMO systems perfectly. The proposed algorithm ensures that all the signals in the closed-loop system are bounded.An adaptive fuzzy control method is proposed for a class of nonlinear MIMO inter-connected systems with uncertainty. Due to the system states are assumed to be unmeasured, the system states are estimated by the observer design. The proposed adaptive law only adjusts on-line the nondeterministic bounds, therefore computing burden on-line is alleviated greatly. The proposed control algorithm can guarantee that all the signals of the closed-loop system are consistently bounded and the tracking errors exponentially converge to a small neighborhood of origin within a small zero area. A simulation example is given to show the effectiveness of the proposed algorithm.An assembled adaptive fuzzy output feedback control scheme is presented for a class of MIMO nonlinear systems with the unmeasured states. This method doesn't require the system state measurable, but an observer is used to estimate the unmeasured states of the systems. The proposed controller consists of the four control components: the combined adaptive fuzzy controller, supervisory controller, supervisory ccontroller, H~∞robust controller and auxiliary compensation item. Compared with the existing results in the observer design, the main advantages of this scheme are that (1) the assembled controller can tradeoff the knowledges of controlled plant and the knowledges of control behavior. (2) the proposed controller has the supervisory control performance. The stability of the closed-loop system is analyzed by using Lyapunov analysis method. Simulation results validate the effectiveness of the proposed combined control scheme.