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This paper investigates an uncertainty optimization problem with stochastic variables. The method of stochastic simulation is used to deal with the uncertain functions. For solving this stochastic model efficiently, a hybrid particle swarm optimization (PSO) algorithm integrated neural network is formulated. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed...
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...
This paper models neural uncertainty using a concept of the agent-based uncertainty theory (AUT). The AUT is based on complex fusion of crisp (non-fuzzy) conflicting judgments of agents. It provides a uniform representation and an operational empirical interpretation for several uncertainty theories such as rough set theory, fuzzy sets theory, evidence theory, and probability theory. The AUT models...
Recently, Adaptive critic design (ACD) has been applied to controller design extensively. It is a powerful approach to cope with the model nonlinearity and uncertainties. Existing ACD-based controllers have been proven as uniformly ultimately bounded (UUB). However, UUB only makes the tracking error converge to a certain bounded region. Although we can minimize the bounded region by increasing the...
In this paper, a new method for adaptive control of nonlinear systems using neural networks and proportional-integral-derivative (PID) methodology is proposed. In this method, a PID control and adaptive linear neural network is used to control a inverted pendulum with uncertainties and external disturbances. The system consists of an inverted pole hinged on a cart which is free to move in the x direction...
In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm for robust controller design, is proposed for a nonlinear system. Utilizing the Lyapunov direct method, controller is shown to be optimal with respect to a cost functional that includes maximum bound on system uncertainty. Controller is continuous and requires the knowledge of the upper bound of system uncertainty...
To obtain much smaller miss distance and even shorter response time, the problem of integrated guidance and control for homing missiles in the pitch plane is considered. As the relative motion equations and the missile dynamics equations are built in different coordinates, the integrated guidance and control model is difficult to build. Also, during the flight, the accurate full-envelope aerodynamic...
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