This paper describes an application of Artificial Neural Networks (ANN) to Load Frequency Control (LFC) of nonlinear power systems. Power systems, such as other industrial processes, have parametric uncertainties that for controller design had to take the uncertainties into account. For this reason, in the design of LFC controller the idea of robust control theories are being used. To improve t...

many real time processes have complex, uncertain and nonlinear dynamics. boilers are nonlinear, time varying, multi-input multi-output (mimo) systems, whose states generally vary with operating conditions. the major problem in controlling that system is that its drum water pressure and steam flow dynamics include an integrator that results a critically stable behavior. conventional controller p...

The control of an unknown multivariable nonlinear process represents a challenging problem. Model based approaches, like Generalized Minimum Variance, provide a flexible framework for addressing the main issues arising in the control of complex nonlinear systems. However, the final performance will depend heavily on the models representing the system. This work presents a comparative analysis o...

this article presents a fuzzy robust mixed - sensitivity gain - scheduled h controller based on the loop -shaping methodology for a class of mimo uncertain nonlinear time - varying systems. in order to design this controller, the nonlinear parameter - dependent plant is first modeled as a set of linear subsystems by takagi and sugeno’s (t - s) fuzzy approach. both loop - shaping methodology and...

Many real time processes have complex, uncertain and nonlinear dynamics. Boilers are nonlinear, time varying, multi-input multi-output (MIMO) systems, whose states generally vary with operating conditions. The major problem in controlling that system is that its drum water pressure and steam flow dynamics include an integrator that results a critically stable behavior. Conventional controller p...

this paper presents a novel neural network controller with composite adaptation low to improve the trajectory tracking problems of biped robots comparing with classical controller. The biped model has 5_link and 6 degrees of freedom and actuated by Plated Pneumatic Artificial Muscle, which have a very high power to weight ratio and it has large stoke compared to similar actuators. The proposed ...

In this paper, an observer based fuzzy adaptive controller (FAC) is designed fora class of large scale systems with non-canonical non-affine nonlinear subsystems. It isassumed that functions of the subsystems and the interactions among subsystems areunknown. By constructing a new class of state observer for each follower, the proposedconsensus control method solves the problem of unmeasured sta...

This paper presents a nonlinear Artificial Neural Networks (ANN) controller based on μ–synthesis for Automatic Generation Control (AGC) of power systems. Power systems such as other industrial plants have some uncertainties and deviations due to multivariable operating conditions and load changes. For this reason, in the design of ANN controller the idea of μ synthesis theory is used. The motiv...

ph control is a challenging problem due to its highly nonlinear nature. in this paper the performances of two different adaptive global linearizing controllers (glc) are compared. least squares technique has been used for identifying the titration curve. the first controller is a standard glc based on material balances of each species. for implementation of this controller a nonlinear state est...