A nonlinear predictive controller is presented in the paper. The greatest advantage of predictive functional control (PFC) is very fast and easy calculation of control variable. Combined with the Takagi-Sugeno (TS) fuzzy model, the PFC becomes nonlinear and is called fuzzy PFC (FPFC). The controller was evaluated by simulation on the well-known process, the continuous stirred-tank reactor (CSTR...

In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is sho...

State estimation and estimator based predictive control of nonlinear autonomous hybrid systems poses a challenging problem as these systems involve discontinuities that are introduced by switching of the discrete variables. In this paper, we propose a state estimation scheme for an autonomous hybrid system using an ensemble Kalman filter (EnKF), which belongs to the class of particle filters an...

The Multivariable Continuous-time Generalised Predictive Controller (CGPC) is recast in a state-space form and shown to include Gen-eralised Minimum Variance (GMV) and an new algorithm, Predictive GMV (PGMV) as special cases. Comparisons are drawn with the exact linearisation methods of nonlinear control and it is noted that the state-space approach extends readily to the nonlinear case. The re...

In this paper, a NCGPC (Nonlinear Continuous Time Generalized Predictive Control) is proposed for a single-link flexible joint manipulator. This control is developed by using a property of NCGPC, the demonstration that the selection of particular design parameters, such as control order and predictor order leads to well-known feedback linearization. Simulations are presented in order to illustr...

In this paper is presented a contribution for development and implementation of nonlinear predictive control based on Hammerstein models as well as to make properties evaluation. In this work, nonlinear predictive control development has been used the time-step linearity method and a compensation term is used with an objective to make better the controller performance. An example demonstrating ...

Abstract : This paper describes a novel technique for scenario generation aimed at closed loop stochastic nonlinear model predictive control. The key ingredient in the algorithm is the use of vector quantization methods. We also show how one can impose a tree structure on the resulting scenarios. Finally, we briefly describe how the scenarios can be used in large scale stochastic nonlinear mode...

This paper presents a globally optimal nonlinear Model Predictive Control (NMPC) algorithm. Utilizing local techniques on nonlinear nonconvex problems leaves one susceptible to suboptimal solutions. In complex problems, local solver reliability is difficult to predict and often highly dependent upon the choice of initial guess. For the purpose of NMPC, local solvers can cause the algorithm to f...

Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on nonlinear programming (NLP) and NLP sensitivity. This met...

A new multivariable controller for cement milling circuits is presented, which is based on a nonlinear model of the circuit and on a nonlinear predictive control strategy. Comparisons with previous LQ control strategies show improved performances with respect to an important source of perturbations of the circuit: a change of hardness of the raw material.