Home About us Contact
|
Home About us Contact | ||
|
|
||
Nonlinear Predictive Control (nonlinear + predictive_control)
Selected AbstractsNonlinear Predictive Control of Fed-Batch Cultures of Escherichia coliCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 7 2010S. Tebbani Abstract A strategy for controlling a fed-batch Escherichia coli culture is described to maintain the culture at the boundary between oxidative and oxido-fermentative regimes. A nonlinear predictive controller is designed to regulate the acetate concentration, constraining the feed rate to follow an optimal reference profile which maximizes the biomass growth. For the sake of simplicity and efficiency, the original problem is converted into an unconstrained nonlinear programming problem, solved by control vector parameterization techniques. The robustness of the structure is further improved by explicitly including the difference between system and model prediction. A robustness study based on a Monte Carlo approach is used to evaluate the performance of the proposed controller. This control law is finally compared to the generic model control strategy. [source] Nonlinear predictive control of smooth nonlinear systems based on Volterra models.INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2010Application to a pilot plant Abstract There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non-autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real-time control of a chemical pilot plant. Copyright © 2009 John Wiley & Sons, Ltd. [source] Output feedback stabilization of constrained systems with nonlinear predictive controlINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3-4 2003Rolf Findeisen Abstract We present an output feedback stabilization scheme for uniformly completely observable nonlinear MIMO systems combining nonlinear model predictive control (NMPC) and high-gain observers. The control signal is recalculated at discrete sampling instants by an NMPC controller using a system model for the predictions. The state information necessary for the prediction is provided by a continuous time high-gain observer. The resulting ,optimal' control signal is open-loop implemented until the next sampling instant. With the proposed scheme semi-global practical stability is achieved. That is, for initial conditions in any compact set contained in the region of attraction of the NMPC state feedback controller, the system states will enter any small set containing the origin, if the high-gain observers is sufficiently fast and the sampling time is small enough. In principle the proposed approach can be used for a variety of state feedback NMPC schemes. Copyright © 2003 John Wiley & Sons, Ltd. [source] Explicit nonlinear predictive control for a magnetic levitation system,ASIAN JOURNAL OF CONTROL, Issue 3 2010A. Ulbig Abstract The paper presents a methodology for the construction of an explicit nonlinear control law via approximation of the nonlinear constrained finite-time optimal control (CFTOC). This is achieved through an approximate mapping of a general nonlinear system in a set of linear piecewise affine (PWA) systems. The key advantages of this methodology are two-fold. First, the construction of an analytic solution of the CFTOC problem leads to an efficient explicit implementation. Second, by taking advantage of model predictive control's systematic fashion to handle constraints, an improved performance can be obtained for the closed-loop system. The proposed theory is applied in real-time for a system with fast dynamics: a magnetic levitation benchmark. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | ||||||||||