Adaptive Control And Stability Analysis For MIMO Nonlinear Systems

The rapid development of modern society and industry promotes the continuous improvement of system control theory to meet the needs of practical application.Considering that modern practical systems contain many nonlinear factors,the control of nonlinear systems has attracted extensive attention.However,due to the diversity and complexity of nonlinear systems and the coexistence of advantages and disadvantages of various control methods,the existing research results of nonlinear systems have some limitations.To overcome the shortcomings of the existing works,it is very necessary to put forward novel control designs.Therefore,this thesis studies multiple input multiple output(MIMO)uncertain nonlinear systems with state constraints or unmeasurable states.Relying on the adaptive backstepping control method,new control schemes and stability analysis are designed.The specific contents include:For uncertain MIMO nonlinear systems subject to full state constraints,the tracking control problem is investigated.By introducing a unified barrier function,the state constraint problem is solved.In the backstepping control process,neural network mechanism and adaptive technology are used to deal with the unknown uncertain functions,and command filter technology is used to avoid the problem of calculation explosion.In addition,in order to save communication resources,the final actual controller is designed as event-triggered controller.Finally,the effectiveness of the tracking control scheme is verified by an inverted pendulum system simulation example and a comparative simulation example.For uncertain MIMO nonlinear systems subject to full state constraints,the tracking control problem is investigated.A generalized barrier function is designed to deal with state constraints.The new barrier function is generalized in the sense that it can be applied to systems with or without state constraints.By designing a new first-order filter and embedding it into backstepping,the structure of the control scheme is feasible and the explosion of calculation is avoided.In addition,a new single parameter adaptive estimation method is constructed to deal with uncertain function vector and unknown gain matrix.Finally,the effectiveness of the tracking control scheme is verified by a manipulator operation simulation example.For non-strict feedback uncertain nonlinear MIMO systems with unmeasured states,the tracking control problem is investigated.A state observer is designed to solve the problem of unmeasured states.Then the control scheme based on state observer is designed.In the control design process,the structural characteristics of neural network are used to approximate the nonlinear function and solve the non-strict feedback problem of the system,and adaptive technology solves the problem of unknown parameters.Additionally,in order to save communication resources,the final actual controller is designed as event-triggered controller.Finally,the effectiveness of the tracking control method is verified by a numerical simulation.For uncertain nonlinear MIMO systems with unmeasured states,unmodeled dynamics and unknown disturbances,the control problem is investigated.Firstly,a neural network observer is introduced to estimate the unmeasured states.Then the control scheme based on state observer is designed.In the process of control scheme design,a dynamic signal is used to deal with the unmodeled dynamics,and then the uncertainties caused by unmodeled dynamics,unknown nonlinear functions and dynamic disturbances are estimated by combining of neural network mechanism and adaptive technology.Finally,numerical simulation example verifies the effectiveness of the control method.