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Adaptive nonlinear output feedback control of GD-FNN(generalized dynamic fuzzy neural network) for underwater robot motion control using forming filter for wave disturbance is presented. This method completely construct nonlinear and uncertain parts of underwater robot by online adaptive learning algorithm without knowing fuzzy neural structure and training phase in advance. Output feedback control...
This paper investigates the estimate of domain of attraction for a class of nonlinear Port-Controlled Hamiltonian (PCH) systems subject to both actuator saturation and disturbances. Firstly, two conditions are established to determine whether an ellipsoid is contractively invariant for the systems only with actuator saturation, with which the biggest ellipsoid contained in the domain of attraction...
This paper presents the design of a robust iterative learning controller for a class of nonlinear systems with non-parametric uncertainties. The unknown nonlinearities are only required to satisfy the local Lipschitz condition. The learning controller is developed, by using both robust control and iterative learning control methods, which enables to compensate for the shortcoming when either one is...
Three kinds of approximators are widely used in adaptive controllers for the nonlinear systems with uncertainty. They are neural network, fuzzy logic systems and wavelet approximator, respectively. By unifying these three approximators, we developed an on-line approximation based controller for a class of uncertain nonlinear systems. It is proved that with the proposed control law and update laws,...
This paper studies the stabilization, L2-disturbance attenuation control and adaptive L2-disturbance attenuation control of a class of nonlinear descriptor systems, and presents a number of new results on the control designs. First, under a suitable state feedback, two sufficient conditions of impulse-free are given for the resulted closed-loop system. Then, the stabilization problem is discussed...
In this paper, a novel adaptive neural network (NN) dynamic surface control(DSC) is developed for a class of strict-feedback nonlinear systems with unknown virtual control gain functions. The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control and introducing integral-type Lyapunov function. Using Young's inequality, only one parameter is adjusted...
An indirect adaptive fuzzy control scheme is developed for a class of uncertain strict-feedback nonlinear systems. The fuzzy approximators are used to design an adaptive fuzzy controller and an adaptive fuzzy identification model, and the unknown parameters are adjusted by the modeling error,the tracking error and its time-varying dead-zone. The size of time-varying dead-zone is adjusted adaptively...
A novel multi-loop nonlinear Internal Model Control (IMC) strategy is developed for MIMO systems under the Partial Least Squares (PLS) framework, which automatically decomposes the MIMO process into several univariate systems in the latent space. The ARX-neural network (ARX-NN) model is incorporated in the PLS subspace and identified via optimizing two parameter sets so that the plant-model mismatch...
Utilizing the notion of Filippov solution, a sliding mode control law is derived for generic second order nonlinear mechanical system with friction. The control law is designed with some tunable parameters and optional functions. The asymptotically stability of the closed-loop system is proved by the concept of the solution, nonsmooth analysis and nonsmooth Lyapunov stability theory. By some numerical...
A gradient flow algorithm model developed for the on-line robust pole assignment is proposed for solving Sylvester equations. The algorithm shows to be capable of synthesizing linear feedback control systems via on-line computing feedback gain matrix and desired closed-loop poles. Meanwhile, the close-loop system matrix is least sensitive to perturbation or uncertainty, and uniformly asymptotically...
This paper discusses the technical structure and utilization of remote supervisory control system. And the novel dual-parallel coupled inverted pendulum platform is constructed. Client-Server architecture is developed with the network communication for closed-loop control. The application allows performing a number of laboratory scale hard real-time control experiments, which can be remotely monitored...
Based on observer, delay-dependent robust H∞ controller design problem for time-delay systems with a class of nonlinear uncertainties is discussed in the paper. Assuming that the nonlinear uncertain functions in the considered model are gain-bounded, we obtain a sufficient condition for the robustly asymptotic stability and H∞ performance of the closed-loop system in the light of Lyapunov stability...
An adaptive nonlinear control method is developed for a class of completely nonaffine pure-feedback systems by the combination of back-stepping design and singular perturbation theory. This strategy can simplify the back-stepping design for the control of pure-feedback system by eliminating the problem of “explosion of complexity”. The virtual/actual control input is derived from the solution of a...
We investigate the decentralized networked stabilization problem for interconnected nonlinear systems via a fuzzy control approach in this paper. Some network characteristics, such as nonuniform sampling, network-induced delays and data packet dropouts, are all considered. State feedback controller is designed and sufficient conditions are derived to guarantee the asymptotic stability of whole closed-loop...
There are five stochastic search algorithms had been designed to optimize the adaptive parameter in predictive functional controller based on Kautz model. They are exhaustive method, local search, particle swarm optimization, chaotic search and genetic optimization. The state stability condition for closed-loop system was given based on Lyapunov stability theory. Their validity had been verified by...
Aiming at a class of strict-feedback nonlinear systems with mismatched uncertainties, an adaptive backstepping fuzzy controller design is presented. By applying backstepping design strategy and online approaching uncertainties with fuzzy approximator, the control inputs and adaptive tuning rules are derived from the Lyapunov stability theory. To deal with the problem of extreme expanded operation...
For the DC chassis dynamometer, a nonlinear mathematical model was established based on the analysis of the transmission system of the DC dynamometer, and an adaptive controller based on RBF NN (radial basis function neural network) was proposed to control a dynamometer to load resistance intelligently to achieve stepless simulation of inertia. By using the Lyapunov synthesis approach, it was proved...
In this paper, adaptive tracking control is investigated for a class of nonlinear time-delay systems with actuator saturation nonlinearity. The uncertain time-delay function is bounded by a nonlinear function with unknown coefficients. The actuator saturation nonlinearity is assumed to be nonsymmetric and unknown. Neural network approximation techniques are utilized to compensate the saturation nonlinearity...
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