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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 mainly focuses on the design and realization of embedded control system based on ARM kernel. Firstly, hardware structure, configuration and signal detection, processing of the control system based on ARM are described. Secondly, the system application programs, including main control program, user operation interface program, keyboard program and development of drivers for some S3C2410X...
This paper proposes a method to design a robust reinforcement learning-based tracking control scheme for the wheeled mobile robot. A policy iteration algorithm and a neural network are used to design an adaptive critic robust controller. A H?? - tracking performance index optimal function is evaluated by this con troller. The stability of the closed-loop system while learning is proven by Lyapunov...
In this paper, we proposed a nonlinear robust control design combined with neural network for a 3 degree of freedom (DOF) helicopter test-bed which may be subjected to unknown external disturbance and contains structure uncertainties. Regulation and tracking control design are proposed for the angles of elevation, pitch and travel axes. Numerical simulation results are provided to illustrate that...
Neural network inverse control is applied to doubly-fed generation system, and the mathematical model of inverse system is derived from the power control model of the doubly-fed induction generator. Through the proper selection of input and output signals of inverse control system and the use of neural network inverse control algorithm, the system is decomposed into two single-variable linear subsystems...
In this paper, we investigate the control design for a class of strict-feedback nonlinear systems preceded by unknown backlash-like hysteresis. Using the characteristics of backlash-like hysteresis, adaptive dynamic surface control (DSC) is developed without constructing a hysteresis inverse. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Function uncertainties...
A discrete time neural network adaptive inversion controller design for a supermaneuverable aircraft nonlinear model is presented. The singular perturbation theory is used to separate the nonlinear dynamics into fast and slow sub-systems; The dynamic inversion is applied to design the control laws for the two sub-systems separately; The neural network adaptive control is based on the dynamic inversion,...
The conventional internal model control and PID (IMC-PID) provides convenient tuning parameter to adjust the response speed and robustness of the closed-loop system because it has only one tuning parameter. But when the characteristics variation and uncertainty factors are included in the control system, it is difficult to accomplish satisfactory control performance by using conventional IMC-PID controllers...
Aiming at a class of nonaffine nonlinear system with uncertainties, an adaptive backstepping neural controller design is presented. By applying backstepping design strategy and online approaching nonlinearity with fully tuned radial basis function (RBF) neural networks, the adaptive tuning rules are derived from the Lyapunov stability theory. A nonlinear tracking differentiator is introduced to deal...
In this paper, a novel model reference adaptive control (MRAC) scheme based on neural network (NN) is proposed for servo system tracking control to achieve high-precision position control. This scheme consists of an MRAC controller and an online NN controller in velocity-loop and a traditional PID controller in position-loop. For reducing influence which arose from modeling error, unknown model dynamics,...
A direct adaptive control design method was proposed for a class of uncertain single-input single-output (SISO) nonaffine nonlinear system using multilayer neural networks (MNNs). The proposed approach used MNNs to approximate and adaptively cancel the unknown part of the inverse functions. Then, Inverse design, backstepping design, and feedback linearization techniques were incorporated to design...
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