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In this paper, stochastic control of nonlinear state space models is discussed. After a brief review on nonlinear state space models, a multi layer perceptron (MLP) neural network is considered to represent the general structure of the controller. Then, an expectation maximization (EM) algorithm joint with the particle smoothing framework are proposed for updating parameters of the MLP network. The...
This paper proposes an adaptive wavelet neuro control (AWNC) system, which is composed of a neural controller and a tangent controller. The neural controller utilizes a wavelet neural network to mimic an ideal controller and the tangent controller is designed to compensate for the approximation error between the ideal controller and the neural controller with using a hyperbolic tangent function. The...
This article presents the implementation of position control of a mobile inverted pendulum(MIP) system by using the radial basis function network(RBF). The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is known as a nonlinear system whose dynamics is non-holonomic. The goal is to control the MIP to maintain the balance of the pendulum while tracking a desired position...
Estimation of plant Jacobian is necessary for successful control of nonlinear systems using neural networks with the specialized learning scheme. Our previous study showed that neuro-emulators provide a better estimation of the plant Jacobian using a new cost function for learning during the course of dynamic modeling and control. This paper presents an approach for further enhancing the estimation...
A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system,...
The neural extended Kalman filter (NEKF) is an adaptive state estimation technique that can be used in target tracking and directly in a feedback loop. It improves state estimates by learning the difference between the a priori model and the actual system dynamics. The neural network training occurs while the system is in operation. Often, however, due to stability concerns, such an adaptive component...
In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.
As linear model predictive control (MPC) becomes a standard technology, nonlinear MPC (NMPC) approach is debuting both in academia and industry. In this paper, the NMPC problem is formulated as a convex quadratic programming problem based on nonlinear model prediction and linearization. A recurrent neural network for NMPC is then applied for solving the quadratic programming problem. The proposed...
The RCA cleaning method is the industry standard way to clean silicon wafers, where temperature control is important for a stable cleaning performance. However, it is difficult mainly because the RCA solutions cause nonlinear and time-varying exothermic chemical reactions. So far, the MSPC (model switching predictive controller) using the CAN2 (competitive associative net 2) has been developed and...
Three-phase AC/DC converter is widely used in many industrial applications. To improve performance, this paper proposes an adaptive neural network based controller design for three-phase PWM AC/DC voltage source converters. The controller is designed based on a nonlinear multi-input multi-output model using Lyapunovpsilas direct method. Since neural networks can approximate unknown nonlinear dynamics,...
The cerebellar model articulation controller (CMAC) neural network is a practical tool for improving existing nonlinear control systems, and it can effectively reduce tracking error of control system. In order to effectively restrain the impact of network delays for wireless networked control systems (WNCS), a novel approach is proposed that modified Smith predictor combined with CMAC-PID control...
This paper presents a systematic approach to solve for the optimal control of a variable-time impulsive system. First, optimality condition for a variable-time impulsive system is derived using the calculus of variations method. Next, a single network adaptive critic technique is proposed to numerically solve for the optimal control and the detailed algorithm is presented. Finally, two examples-one...
In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm is proposed for a bilinear system. Utilizing the Lyapunov direct method, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the system states. In the proposed algorithm, Neural Network (NN) is used to find approximate solution of HJB equation...
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