This work was supported in part by the Office of Naval Research under Grant F-149. Abstract An experimental and theoretical comparison of passivity-based and input-output linearization controllers for induction motors is presented. The comparison is done taking into account saturation of the magnetic material in the main flux path of the induction motor. Experimental results show that the use ...

Black-box modeling techniques based on artificial neural networks are opening new horizons for modeling and controlling nonlinear processes in biotechnology and chemical process industries. The link between dynamic process models and actual process control is provided by the concept of model based control (MBC), e.g. Internal Model Control (IMC) or Model Based Predictive Control (MBPC). To avoi...

In this paper, a novel method is developed for designing the output feedback controller for Static Synchronous Series Compensator (SSSC). In the proposed method, the problem of selecting the output feedback gains for the SSSC controllers is changed into an optimization problem with a time domain-based objective function.Then, it is solved by using the particle swarm optimization (PSO) algorithm...

This paper presents the design of feedback linearization and neural network based feedback linearization (NARMAL2) controller for a magnetic levitation system. The magnetic levitation system is one of the classical nonlinear systems. The paper provides simulation results to validate the theoretical design.

the aim of this paper is to investigate a novel approach for output feedback damping controller design ofstatcom in order to enhance the damping of power system low frequency oscillations (lfo). the design ofoutput feedback controller is considered as an optimization problem according with the time domain-basedobjective function which is solved by a honey bee mating optimization algorithm (hbmo...

This paper presents a robust adaptive feedback linearizing control law to solve the integrated kinematic and dynamic trajectory tracking problem of nonholonomic mechanical systems in presence of parametric and nonparametric uncertainties. An adaptive nonlinear control law is proposed based on input-output feedback linearization technique to get asymptotically exact cancellation of the parametri...

We propose a formulation for approximate constrained nonlinear output-feedback stochastic model predictive control. Starting from the ideal but intractable optimal control problem (OCP), which involves optimization over output-dependent policies, we use linearization with respect to uncertainty derive tractable approximation includes knowledge of output model. This allows us compute expected va...