Adaptive Learning Control For Bilinear Parametric Nonlinear Systems With Time - Varying Delays

Currently in the actual nonlinear systems, the prevalence of self-complexity anduncertainty caused by external interference, studying the control method to make thesystem have a better the stability and robustness has great significance both in theoryand applications. The actual control system has certain characteristics, time-varyingor time invariant and other uncertainties, such as exist in the system model withunknown parameters, characteristics and performance of the system, if ignored, willgreatly reduce the performance of the system in actually works.To uncertainties of nonlinear systems,one of the effective control method is theuse of adaptive learning control(ALC),adaptive learning control for nonlinear systemsuncertainties time-varying parameters, designing the adaptive learning control lawand time-varying parameter learning law, to solve repetition trajectory trackingproblem of nonlinear systems in the finite interval. So far, adaptive learning controlhas achieved fruitful results,but there are some problems to be solved. As theuncertainty of nonlinear systems with unknown time delay and time-varying delay,delays exist in nonlinear systems, will make the systems unstable, thereby reducingperformance and requirements of nonlinear systems. Therefore,with the deepening ofone of the nonlinear characteristics of the nonlinear systems analysis, and thedevelopment of adaptive control theory, nonlinear system of international anddomestic uncertainties contain unknown time-varying delay problem in terms ofrobustness, achieved fruitful results.In this thesis, we use ALC method for a class of nonlinear systems withuncertainties unknown time delay and time-varying delay, using Lyapunov stabilitytheory, constructed lyapunov functionals, design adaptive learning control law andtime-varying parameter learning law make the tracking error asymptotically tends tozero, thereby enhancing the stability of the nonlinear system.In order to verify the stability of the algorithm, using matlab simulation, shows the effectiveness and feasibility of the algorithm, the basic idea of the algorithm canbe applied to such as the robot system, the inverted pendulum system and so on.