Aerobics has been broadly adopted in colleges and universities in our country. For aerobics can be better able to carry out, the performance prediction is very important. Aiming at the problems that the predictive model is often mismatching and difficult to solve the nonlinear optimization function of nonlinear system model of predictive control, an online support vector machine (OSVM) modeling...

This paper compares the identification rates of a speaker recognition system using several parameterizations, with special emphasis on the residual signal obtained from linear and nonlinear predictive analysis. It is found that the residual signal is still useful even when using a high dimensional linear predictive analysis. On the other hand, it is shown that the residual signal of a nonlinear...

The compartmental models, as Hovorka’s one, are usually exact but complicated. Thus, they are not suitable for direct usage in nonlinear predictive controllers because of complexity of the resulting controller and numerical problems that may occur. Thus, simplified nonlinear (neural and fuzzy) models are developed in this paper for the future use in the predictive algorithms. Training and struc...

-This paper presents a nonlinear generalised predictive control scheme for a squirrel cage motor without speed sensor. Some of the difficulties faced are due to uncertainties in the parameters measurements, high cost to obtain state measurements in addition to the inherently non-linear behaviour. The control scheme presented is composed of a positioning predictive control and an open loop obser...

Over the recent years many advances in the area of nonlinear model predictive control have been made. However, with respect to the output feedback problem and predictive control only a limited number of results are available. Most of the existing approaches do only guarantee local stability. In this note we propose the use of a high gain observer in combination with a sampled nonlinear predicti...

At the end of the 19th century Liouville and Riemann introduced the notion of a fractional-order derivative, and in the latter half of the 20th century the concept of the so-called Grünewald-Letnikov fractional-order discrete difference has been put forward. In the paper a predictive controller for MIMO fractional-order discrete-time systems is proposed, and then the concept is extended to nonl...

This paper is concerned with neural-learning control of nonlinear dynamical systems. A variable neural network is introduced for approximating unknown nonlinearities of dynamical systems. Based on variable neural networks, adaptive neural control and predictive neural control schemes are studied. In the adaptive neural control scheme, the weight-learning laws and adaptive controller developed u...

This paper presents an explorative work about a Model Predictive Control (MPC) technique. A nonlinear model predictive controller was designed and applied to a fedbatch bioprocess. First, bioprocess and its model are described. Then, a controller based in a nonlinear MPC scheme (Dual Fuzzy Model Predictive Control DF-MPC) is proposed with the aim to test the control of substrate concentration i...

A scheme for temperature control of a greenhouse is proposed. The present work proposes an approach based on a combination of two different control schemes: Feedback Linearization (FL) and standard linear Model Predictive Control (MPC), using their advantages. The treated greenhouse is considered a nonlinear Single-Input Single-Output process and subjected to strong external disturbances. Since...

This work focuses on model predictive control of nonlinear singularly perturbed systems. A composite control system using multirate sampling (i.e., fast sampling of the fast state variables and slow sampling of the slow state variables) and consisting of a “fast” feedback controller that stabilizes the fast dynamics and a model predictive controller that stabilizes the slow dynamics and enforce...