| Many physical systems can be modeled as a class of strict feedback nonlinear systems,so the control of strict feedback nonlinear systems has attracted the attention of many scholars.In modern manufacturing industry,intelligent and unmanned is the main trend of development,and robotic manipulator is the most commonly used in manufacturing production.The robotic manipulator can be modeled as an uncertain strictly feedback nonlinear system,so the robotic manipulator can be regarded as a special class of strictly feedback nonlinear systems.Nonlinear systems often contain multiple nonlinear characteristic components,which have a significant impact on the performance of the system,so it is of practical significance to study the control problem of strictly feedback nonlinear systems and robotic manipulator systems.In summary,this thesis takes strict feedback nonlinear system and robotic manipulator system as the research object,and uses advanced control theories,such as fixed time control,faulttolerant control and constrained control methods,to discuss their tracking control problems.The research content of this thesis is as follows:(1)Firstly,the problem of adaptive fuzzy fixed-time fault-tolerant control for strictly feedback nonlinear systems with unknown nonaffine nonlinear faults is studied.To address the nonaffine nonlinear fault control design matter,the Butterworth filter signal is used in the control design process.The “explosion of complexity” is avoided by adopting command filter method and the filter errors can be eliminated by introducing compensating signals.The fuzzy logic system is used as an approximator to estimate the unknown nonlinear function in the system.Using Lyapunov stability principle,we prove that the closed-loop system is semi-global fixed-time stable,and the tracking error can converge to a small neighborhood of zero in a fixed-time that the convergence time is not affected by the initial conditions of the system.The simulation results show the effectiveness of the proposed control method.(2)Secondly,an adaptive neural network tracking control method is proposed for the robotic manipulator with unknown time-varying delay.In order to work out the effect of unknown time-varying delays on the robotic manipulator,the appropriate Lyapunov-Krasovskii functionals and separation technology are chosen to settle this matter.By utilizing command filtering technology,the repeated derivation of virtual controller can be avoided in the controller design process.In this thesis,Lyapunov stability analysis can prove that all signals of the closed-loop system are semi-global uniformly ultimately bounded and the tracking error can converge to a compact neighborhood with respect to zero.The simulation consequences demonstrate the availability of the put forward control approach.(3)Finally,the adaptive neural network tracking control problem for asymmetric time-varying full-state constrained robotic manipulator with input dead zone is studied.A new adaptive neural controller is designed by simultaneously considering the dynamics of the manipulator and motor,time-varying asymmetric barrier Lyapunov functions and input dead zone.It is well known that the constraint boundary is usually a constant or time-varying function in the past study of nonlinear system with state constraints.In this thesis,the constraint boundaries are considered to be related to both state and time.In order to compensate the negative impact of input dead zone on the system,the dead zone function is divided into two items to be processed.Lyapunov stability analysis testifies all signals in the closed loop system are bounded,the tracking error can converge to a small set around zero,and time-varying full-state constraints are never violated.The simulation consequences illustrate the effectiveness of the proposed control method. |
