Transient And Steady State Performance Constraints Control For Nonlinear Systems In Lower-triangular Form

With the rapid development of modern industry,science and technology,many practical engineering control systems are becoming more and more complicated.They often show serious uncertainties,nonlinearity,strong coupling,multi-delay and other characteristics.At the same time,the demands of production speed,production quality,product accuracy and safe operation of the industrial process are getting higher and higher.Therefore,the transient and steady state performance constraints control problem of the complex nonlinear dynamic system becomes the hot and difficult point of the control field.In this paper,the problem of controller design for several classes of complex nonlinear dynamical systems in lower-triangular form with state variables or output variable constraints is studied.The main research works are summarized as follows:Firstly,we investigate the output feedback prescribed performance control for a class of strict feedback nonlinear time-delay systems with complex unmodeled dynamics.For the case that the states are unmeasured,we introduce a time delay-independent reduced-order observer.By using the idea of changing supply function,we choose a proper Lyapunov function to deal with the effects of unmodeled dynamics on the system.Based on the symmetric prescribed performance transformation and Lyapunov-Krasovskii functional,a memoryless output feedback transient and steady state performance constraints controller is proposed and the asymptotic stability of the system is proved.Secondly,for a class of uncertain interconnected systems consisting of multiple strict feedback nonlinear time-delay systems with unmodeled dynamics,the problem of decentralized adaptive neural network prescribed performance control is studied.For the asymmetric predetermined performance demand,the singularity problem caused by the asymmetric predetermined performance control design is avoided by a novel state transformation.By using the neural network and the backstepping algorithm,delay-independent reduced order observer based output feedback prescribed performance controllers are proposed to ensure the transient and steady state performance of the system outputs.Then,the decentralized dynamic output feedback tracking prescribed performance control is investigated for a class of stochastic interconnected nonlinear time-delay systems with lower triangular structures.Considering the nonlinear functions in the system are known and unknown respectively,we study the asymmetric prescribed performance tracking control problem and two novel output feedback based controllers are proposed to guarantee the uniformly ultimately boundedness stability in probability of the system.In particular,neural networks are used when the nonlinear functions are unknown.Furthermore,for a class of multi-agent systems composed of strict feedback nonlinear systems,we study the leader-following consensus tracking prescribed performance control strategy in the presence of external disturbance and unmeasured states.First,we design reduced-order observers to estimate the unmeasured state variables of the followers online.Then,based on the prescribed performance transformation of consensus tracking error,the distributed output feedback control strategies are proposed by combining the backstepping technique with the dynamic surface control technology and the stability of the system is proved by theoretical method and simulation experiment.Finally,we study the distributed adaptive fuzzy leader-following containment tracking performance constraint control for a class of high-order stochastic pure-feedback nonlinear multi-agent systems with multiple dynamic leaders,unknown nonlinearity and quantized inputs.First,the mean value theorem is used to transfer the non-affine part of the system into affine form.Based on the novel barrier Lyapunov function in tangent form,the distributed state feedback adaptive intelligent control strategies are proposed,and the bounded stability in probability of the system is proved.