In this article, adaptive neural network control of coordinated manipulators is considered in an effort to eliminate the time-consuming and error prone dynamic modeling process which is necessary for the implementation of conventional adaptive control. After a concise dynamic model in the object coordinate space is developed for the coordinated manipulators, an adaptive neural network controlle...

Neural network model predictive controllers have demonstrated high potential in the non-conventional branch of nonlinear control. However, the major issue in process control of nonlinear systems is the sensitivity to parameters variations and uncertainties. Indeed, when the process is controlled by neural network model predictive control (NNMPC) and subject to parameters variations or uncertain...

This paper presents a method to integrate adaptive estimation and adaptive control designs for a class of uncertain nonlinear systems having both parametric uncertainties and unmodeled dynamics. The method is based on Lyapunov-like stability analysis of all the errors in the closed-loop system. The adaptive estimator considered is a linear, time-varying Kalman filter augmented by the output of ...

An adaptive robust tracking controller is proposed for robot systems under plant uncertainties and external disturbances. Nonlinear robust control theory and neural network design are combined to construct a hybrid adaptive-robust tracking control scheme which ensures that the joint positions track the desired reference signals. Neural network is used to identify the uncertainties, and the effe...

in the present work, the influences of temperature, solvent concentration and ultrasonic irradiation time were numerically analyzed on viscosity reduction of residue fuel oil (rfo). ultrasonic irradiation was applied at power of 280 w and low frequency of 24 khz. the main feature of this research is prediction and optimization of the kinematic viscosity data. the measured results of eighty-four...

If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliancesjust to name a few are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods a neural network reference c...

We present a tool to estimate the performance of the neural network in a neural network based adaptive controller. Using a Bayesian approach, this tool supports verification and validation of the adaptive controller as well as on-line monitoring. In this paper, we discuss our approach and present simulation results using the adaptive controller developed for NASA’s IFCS (Intelligent Flight Cont...

Typical neural-adaptive control approaches update neural-network weights as though they were adaptive parameters in a continuous-time adaptive control. However, requiring fast digital rates usually restricts the size of the neural network. In this paper we analyze a deltarule update for the weights, applied at a relatively slow digital rate. We show that digital weight update causes the neural ...