The paper discusses economic performance design under nonlinear dynamic behavior and changing operating conditions. Two methods to control plants with input-induced nonlinearities in the presence of hard constraints are presented. A nonlinear predictive controller is designed based on a nonlinear dynamic parameter-dependent model of the system for tracking control. Next a decomposition of the model of the plant is made which allows nonlinear optimal static feedforward and linear dynamic feedback. Model predictive control (MPC) was selected to control the transients while satisfying all constraints without loss of performance. The control scheme has been applied on an industrial printing system. Simulation examples show the effectiveness of the proposed approaches in the presence of variation in the input print queue and constraints.
The research focuses on a state estimation problem for the paper path and heat flow control of industrial printers. The dynamics of industrial printers can be modeled with a system with input-induced nonlinearities. This model of the system switches in time with a parameter vector which is externally triggered. An important issue in the design of the state estimator involves the question as to what extend the stability and the performance of the estimated dynamics are robust against perturbations and uncertainties in the parameters of the system. For this class of systems we propose the design of an estimator based on an affine parameter dependent representation of the system with parametric uncertainties. The performance of the estimator is analyzed and the stability proven based on Lyapunov theory. Simulation examples show the effectiveness of the proposed approach in the presence of several arbitrary switches between modes.
In this paper we consider the design of a nonlinear predictive controller based on a nonlinear dynamic model of the system so as to achieve tracking control for systems with input-induced nonlinearities. The economic performance under nonlinear dynamic behavior and changing operating conditions is discussed. The closed-loop properties are analyzed under suitable assumptions on the model of the plant. Control strategies are discussed and compared to different implementations in the literature. Simulation examples show the effectiveness of the proposed approaches. The approach has been validated on the example of an industrial printing system with large variations in the input print queue.
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