Model-based control design for AVC

2011 ◽  
pp. 135-158
Author(s):  
S. Veres
Keyword(s):  
Control Design ◽  
Model Based Control ◽  
Model Based

scholarly journals A Model-Based Control Design Approach for Linear Free-Piston Engines

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
T. N. Kigezi ◽  
J. F. Dunne
Keyword(s):  
Control Design ◽  
Linear Quadratic Regulator ◽  
Design Approach ◽  
Linear Quadratic ◽  
Free Piston ◽  
Model Based Control ◽  
Model Based ◽  
Bottom Dead Center ◽  
Free Piston Engine ◽  
Advanced Control

A general design approach is presented for model-based control of piston position in a free-piston engine (FPE). The proposed approach controls either “bottom-dead-center” (BDC) or “top-dead-center” (TDC) position. The key advantage of the approach is that it facilitates controller parameter selection, by the way of deriving parameter combinations that yield both stable BDC and stable TDC. Driving the piston motion toward a target compression ratio is, therefore, achieved with sound engineering insight, consequently allowing repeatable engine cycles for steady power output. The adopted control design approach is based on linear control-oriented models derived from exploitation of energy conservation principles in a two-stroke engine cycle. Two controllers are developed: A proportional integral (PI) controller with an associated stability condition expressed in terms of controller parameters, and a linear quadratic regulator (LQR) to demonstrate a framework for advanced control design where needed. A detailed analysis is undertaken on two FPE case studies differing only by rebound device type, reporting simulation results for both PI and LQR control. The applicability of the proposed methodology to other common FPE configurations is examined to demonstrate its generality.

scholarly journals State feedback fuzzy-model-based control for Markovian jump nonlinear systems

2004 ◽  
Vol 15 (3) ◽  
pp. 279-290 ◽  
Author(s):  
Natache S. D. Arrifano ◽  
Vilma A. Oliveira
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy System ◽  
System Modeling ◽  
Fuzzy Model ◽  
Control Design ◽  
Linear Matrix ◽  
Markovian Jump ◽  
Stabilizing Controller ◽  
Model Based Control ◽  
Model Based

This paper deals with the fuzzy-model-based control design for a class of Markovian jump nonlinear systems. A fuzzy system modeling is proposed to represent the dynamics of this class of systems. The structure of the fuzzy system is composed of two levels, a crisp level which describes the Markovian jumps and a fuzzy level which describes the system nonlinearities. A sufficient condition on the existence of a stochastically stabilizing controller using a Lyapunov function approach is presented. The fuzzy-model-based control design is formulated in terms of a set of linear matrix inequalities. Simulation results for a single-machine infinite-bus power system which is modeled as a Markovian jump nonlinear system in the infinite-bus voltage are presented to illustrate the applicability of the technique.

Application of an Exhaust Geometry Based Delay Prediction Model to Internal Combustion Engines

2009 ◽  
Author(s):  
Jason Meyer ◽  
Sai S. V. Rajagopalan ◽  
Shawn Midlam-Mohler ◽  
Stephen Yurkovich ◽  
Yann Guezennec
Keyword(s):  
Steady State ◽  
Internal Combustion Engines ◽  
Control Design ◽  
Delay Model ◽  
State Delay ◽  
Model Based Control ◽  
Engine Model ◽  
Model Based ◽  
Robust Control Design ◽  
Delay Prediction

All vehicle manufacturers implement an air-to-fuel ratio (AFR) control system for emissions reduction in gasoline engines. When using a model based control structure, it is vital to capture the underlying dynamics of the plant as accurately as possible, thus facilitating a robust control design that meets the emissions regulation requirements. One of the leading sources of uncertainty in the engine model is the variable plant delay. Although the delay could be modeled using a look-up table of steady-state delay values, during transients when AFR control is most important the steady-state delay poorly approximates the true delay. An exhaust geometry based delay model was developed previously within the framework of a model based control design for AFR control of stoichiometric engines. In this paper, it is shown that using this model the delay can be predicted with a significantly higher accuracy especially during transients, thus improving emissions performance. Because the plant delay plays a destabilizing role in feedback control, the utility of such a model is also to minimize phase errors between the predicted and measured equivalence ratio (EQR) in a reference tracking control setting.

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