In this paper the distributed asymptotic consensus problem is addressed for a group of high-order nonaffine agents with uncertain dynamics,nonvanishing disturbances and unknown control directions under directed networ...In this paper the distributed asymptotic consensus problem is addressed for a group of high-order nonaffine agents with uncertain dynamics,nonvanishing disturbances and unknown control directions under directed networks.A class of auxiliary variables are first introduced which forms second-order filters and induces all measurable signals of agents’states.In view of this property,a distributed robust integral of the sign of the error(DRISE)design combined with the Nussbaum-type function is presented that guarantees not only the desired asymptotic consensus,but also the uniform boundedness of all closed-loop variables.Compared with the traditional sliding mode control(SMC)technique,the main feature of our approach is that the integral operation in the proposed control algorithm is designed to be adopted in a continuous manner and ensures less chattering behavior.Simulation results for a group of Duffing-Holmes chaotic systems are employed to verify our theoretical analysis.展开更多
In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to m...In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and algebraic parts.In the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.展开更多
Adaptive neural network (NN) dynamic surface control (DSC) is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of "explosion of complexity" and circular constructio...Adaptive neural network (NN) dynamic surface control (DSC) is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of "explosion of complexity" and circular construction of the practical controller in the traditional backstepping algorithm are avoided by using this controller design method. For removing the requirements on the sign of the derivative of function f~, Nussbaum control gain technique is used in control design procedure. The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals. Proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Two simulation examples are presented to demonstrate the method.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(61973074,61921004,U1713209).
文摘In this paper the distributed asymptotic consensus problem is addressed for a group of high-order nonaffine agents with uncertain dynamics,nonvanishing disturbances and unknown control directions under directed networks.A class of auxiliary variables are first introduced which forms second-order filters and induces all measurable signals of agents’states.In view of this property,a distributed robust integral of the sign of the error(DRISE)design combined with the Nussbaum-type function is presented that guarantees not only the desired asymptotic consensus,but also the uniform boundedness of all closed-loop variables.Compared with the traditional sliding mode control(SMC)technique,the main feature of our approach is that the integral operation in the proposed control algorithm is designed to be adopted in a continuous manner and ensures less chattering behavior.Simulation results for a group of Duffing-Holmes chaotic systems are employed to verify our theoretical analysis.
文摘In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and algebraic parts.In the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.
基金partially supported by the Key Program of Henan Provincial Department of Education(No.13A470254)National Natural Science Foundation of China(Nos.61273137 and 51375145)+1 种基金the Science and Technology Innovative Foundation for Distinguished Young Scholar of Henan Province(No.144100510004)the Science and Technology Programme Foundation for the Innovative Talents of Henan Province University(No.13HASTIT038)
文摘Adaptive neural network (NN) dynamic surface control (DSC) is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of "explosion of complexity" and circular construction of the practical controller in the traditional backstepping algorithm are avoided by using this controller design method. For removing the requirements on the sign of the derivative of function f~, Nussbaum control gain technique is used in control design procedure. The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals. Proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Two simulation examples are presented to demonstrate the method.