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This paper develops two proportional-type networked iterative learning control (NILC) schemes for a class of linear-time-invariant systems with stochastic packet dropout being subject to Bernoulli-type distribution. In the NILC schemes, we consider two types of compensation algorithms for dropped data: one of which is to replace the dropped data by that of the successfully captured at the concurrent...
This paper presents a finite-time observer for linear time-delay systems. In contrast to many observers, which normally estimate the system state in an asymptotic fashion, this observer estimates the exact system state in predetermined finite time. The finite-time observer proposed is achieved by updating the observer state based on actual and pass data of the observer. Simulation results are also...
This paper investigates the discounted infinite horizon optimal control problem for the stochastic multi-valued logical dynamical systems with finite states. After giving the equivalent descriptions of the stochastic logical dynamical system in terms of Markov decision process, the infinite horizon optimization problem is presented in an algebraic form. Based on the semi-tensor product of matrices...
For ARMAX models, a Latest-Estimation Based Hierarchical Recursive Extended Least Squares algorithm is presented in this paper. The basic idea is to make full use of the latest estimation, and combine this with the hierarchical idea. In the proposed algorithm, the estimates of the white noise information vector is updated by using the latest estimation. The convergence performance of the proposed...
This paper investigates the problem of relative control for spacecraft formation flying on an arbitrary elliptical orbit. The nonlinear dynamic model describing the relative motion between the leader spacecraft and the follower spacecraft is established in the presence of system model uncertainty and external disturbance. A continuous finite-time control scheme is designed by using a new form of terminal...
This paper considers the global finite-time output-feedback stabilization for a class of uncertain nonlinear systems. Essentially different from the existing related literature, the systems in question allow serious parametric unknowns and serious time-variations coupling to the unmeasurable states, and possess remarkably inherent nonlinearities allowing to be low-order and high-order with respect...
This paper focuses on adaptive neural control of robot manipulator with unknown system dynamics under the limitation of prescribed performance. A performance function is introduced to express the prescribed constraints of tracking errors. Subsequently, a performance transformation method is proposed to solve the problem of the prescribed performance. The unknown dynamics of robot are approximated...
This paper considers the problems of finite time stability and stabilization for nonlinear singular systems. First, the Lyapunov direct method and the existing results are extended to study the finite time stability of nonlinear singular systems, some sufficient conditions are given and the estimations of settling times are also derived. Then, a finite time control law for nonlinear singular systems...
This paper presents some further results on sliding mode control for a class of nonlinear systems with bounded uncertain parameters. A finite-time sliding manifold is proposed by incorporating piecewise defined function of time into the existing terminal sliding manifold, based on which a novel time-varying finite-time sliding mode controller is proposed. The proposed control strategy eliminates the...
In this paper, an output regulation problem with unknown-input driven exosystem is formulated and solved for a class of nonlinear plant with unity relative degree. To handle both bounded and unbounded exosystems, we relaxed the asymptotic convergence requirement on the closed-loop state to its associated steady-state and proposed an approximated regulator-equation condition. By using changing supply...
In this paper, we construct a nonlinear extended state observer (ESO) through nonlinear functions switching between sub-linear and linear regions. The convergence is proved with explicit error estimation. The numerical simulations are carried out for comparing the performance of different ESOs. The simulation visualization shows that the proposed ESO in this paper takes advantages of low peaking value...
A new backstepping control scheme for piezoactuator-driven stage using prescribed performance is proposed. The piezoactuator-driven stage's hysteresis behavior is described by Bouc-Wen model. A prescribed performance bound is used to tackle the tracking error's convergence rate and maximum overshoot. H-infinity robust control is used to improve the robust controllability of the piezoactuator-driven...
Finite-time state consensus problems for multi-agent systems affected by bounded disturbances are investigated under directed communication topology. Distributive protocols which are presented in this paper can ensure that the agents' states reach an agreement in finite time. Such solution is made possible by the newly defined Laplacian matrix and integral sliding mode(ISM). The effectiveness of the...
Without the in-degree balanced assumption, the group consensus protocol design problem is discussed for multi-agent systems with double-integrator dynamics. Based on the dynamic model of double-integrator system, a group consensus control protocol containing the couple coefficient γ is designed. The convergence analysis is discussed and the sufficient conditions of group convergence for double-integrator...
This paper aims at design and analysis of iterative learning control (ILC) for a class of nonlinear wave equations. Without any simplification or discretization of the 3D dynamics in the time, the space as well as the iteration domains, the learning convergence of ILC is analyzed for the wave equations directly by virtue of the contraction mapping methodology and λ-norm. It is shown that the proposed...
A robust diffusion adaptive filtering algorithm, called the diffusion recursive least lp-norm (DRLP), is developed for distributed estimation over network. The new algorithm aims at recursively minimizing the lp-norm of error, and can offer a more stable and robust solution than traditional adaptive filtering schemes based on minimization of the squared error, such as the diffusion recursive least...
This paper is concerned with the problem of almost sure exponential stability and moment exponential stability for a class of stochastic parabolic complex neural networks driven by space-time white noise. The sufficient conditions ensuring the almost sure exponential stability and moment exponential stability of the systems are developed by using strong Itô function and Nonnegative semi-martingale...
Identifying an accurate model of the stagnation pressure on-line will benefit the design of the controller and improve the quality of the wind tunnel experiments. This paper focuses on the recursive identification of the stagnation pressure in the wind tunnel system. Based on the study of the aforementioned system structure, the stagnation pressure system is expressed by a block oriented model with...
In this paper, a new concept of iterative learning control (ILC), namely consolidated ILC (CILC), is introduced. An iterative learning control system is subject to data interrupt when the system output is restricted within a certain area. Data interrupt means that the system output is interrupted at a certain moment due to output restrictions. The output data from this moment to the end of the trail...
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