Adaptive Control Of Nonstrict Feedback Nonlinear Systems With Unknown Control Gain

With the rapid development of science and technology,realizing efficient control through network has become a research hotspot in the field of automatic control.However,various complex network environments bring great challenges to the control research of the system.For example,the lack of network communication resources or limited computing power has become a hot issue in control research.In addition,in view of the operating errors in industrial manufacturing and aging of the components,the system actuator will inevitably fail,so it is very necessary to design a fault-tolerant scheme to ensure that the control can proceed smoothly.Adaptive control has good learning,adaptation and adjustment ability,which is of great significance to the control research of all kinds of complex systems.Focusing on the above problems,in this thesis,based on a class of nonstrict feedback nonlinear systems with unknown control gain,the adaptive control is carried out with the help of backstepping technology and Lyapunov stability theory under the condition of limited communication resources,actuator dead zone or Bouc-Wen hysteresis fault.Specifically,it includes the following aspects:1.Based on nonstrict feedback stochastic nonlinear system with unknown control gain,an adaptive fuzzy fault tolerant control scheme with dead zone input is presented.Firstly,an assumption is used to deal with the influence of unknown control gain in the system.Secondly,fuzzy logic system is introduced to handle the problems of unknown nonlinear function and nonstrict feedback structure.Considering the asymmetric dead zone input,an adaptive tracking controller is constructed by utilizing the backstepping technology.The controller can not only achieve the tracking target with small tracking error,but also guarantee that all closed-loop signals are bounded in probability.Finally,the effectiveness of the method is verified by the mathematical model of the manipulator system.2.Based on nonstrict feedback stochastic nonlinear system with unknown control directions,an event triggered adaptive fuzzy control scheme is presented.Firstly,a linear state transformation is introduced to facilitate the control design,and then a composite unknown control coefficient is compensated by Nussbaum function.Furthermore,with the help of fuzzy logic system and its structural characteristics,the design obstacles caused by the nonstrict feedback structure are settled,the assumption that the nonlinear function needs to meet the global Lipschitz is avoided,and the conservatism is reduced.Based on the event trigger mechanism of relative threshold,an event triggered controller is devised.The controller not only saves communication resources,but also ensures that all closed-loop signals are bounded in probability.Finally,the effectiveness of the method is verified by a mathematical model of ship dynamics system.3.Based on nonstrict feedback nonlinear system with external disturbance and unknown control directions,an adaptive neural network fault-tolerant control scheme is constructed.Firstly,by introducing linear state transformation and a new coordinate transformation,the oscillation phenomenon caused by compensating unknown control coefficients by Nussbaum function is avoided.Then,the design method of disturbance observer is improved by introducing auxiliary signal and defining disturbance estimation.The constructed disturbance observer has the characteristics of low coupling and high cohesion.In addition,considering the Bouc-Wen hysteresis fault of the system actuator,an adaptive fault tolerant controller is devised by using neural network and backstepping technology.The designed controller ensures that all closed-loop signals are bounded,and the system output,state observation error and disturbance observation error converge near the origin.Finally,the effectiveness of the scheme is verified by a mathematical model of Nomoto nonlinear ship system.