Optimization of PID controller parameters has been a hot issue in the fields of Automatic control. In the automatic control process, the controlled object has nonlinear and uncertainty characteristics. Traditional PID parameters methods are often time-consuming and difficult to obtain control effect, causing the control accuracy not high. In order to solve the optimization problem of PID contro...

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...

Because simulation turntable servo system is highly nonlinear and uncertainty plants, a fuzzy neural network PID controller is proposed based on the Radial Basis Function (RBF). Up to now, various kinds of nonlinear PID controllers have been designed in order to satisfactorily control this system and some of them applied in actual systems with different degrees. Given this background, the step ...

In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult be...

This paper presents a Multilayer Neural Network controller for real time control applications. A model reference structure is developed and a neural network is used as a compensator in the closed loop system. This scheme can be used in the control of nonlinear systems and/or as an adaptive controller if desired.

We present two neural network controller learning schemes based on feedbackerror-learning and modular architecture for recognition and control of multiple manipulated objects. In the first scheme, a Gating Network is trained to acquire object-specific representations for recognition of a number of objects (or sets of objects). In the second scheme, an Estimation Network is trained to acquire fu...

Microgrid control in isolated mode is a highly important subject area. In the present paper, a new method is used for controlling the isolated microgrids. This method was used based on the classification of the microgrids into two groups, namely fast-dynamic (battery and flywheel) and slow-dynamic (diesel generator, electrolyzer, fuel cell). For the microgrid components with fast dynamics, a se...

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechan...

A novel approach to neural network based tracking-control of robot manipulator including actuator dynamics is proposed by using of backstepping method. A simple two-step backstepping is considered for an nlink robotic system, and a feedforward neural controller is designed at second step where structured and unstructured uncertainties in robot dynamics and actuator model are approximated by thi...

This work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. Considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. By linearizing this mode, a sliding mode controller is designed. The linearized mode is subject to uncertainties. The uncertainties are generated in the process of linear...