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Nonlinear Model Predictive Control
2007In general, industrial processes are nonlinear, but, as has been shown in this book, most MPC applications are based on the use of linear models. There are two main reasons for this: on one hand, the identification of a linear model based on process data is relatively easy and, on the other hand, linear models provide good results when the plant is ...
E. F. Camacho, C. Bordons
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Nonlinear model predictive control
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999Nonlinear model predictive control (NMPC) has been introduced in commercial applications. Since mid 1996 approximately 50 applications have been commissioned in polymers, chemicals, food, pulp and paper, and oil refining. This industrial presentation provides a brief overview of the commercial NMPC package and presents a summary of a specific polymers ...
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Nonlinear Model Predictive Control with aggregated constraints
Automatica, 2022In the paper, a nonlinear model predictive control (NMPC) formulation with aggregated constraints approach is introduced. Constraint aggregation function lump the original constraints of the control problem into a reduced set of nonlinear constraints. Then significant saving in the computational footprint of solving the MNPC can be achieved. The effect
de Freitas Virgílio Pereira, Mateus +2 more
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Nonlinear Model Predictive Control
2021The nonlinear model predictive control (NMPC) approach allows more challenging control problems to be handled than those dealt with by the linear model predictive control (LMPC) method. For engine control problems, the consideration of their nonlinear process behavior is particularly important as their treatment as a linearized system is often not ...
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Nonlinear Multi-Model Predictive Control
IFAC Proceedings Volumes, 1996Abstract A kind of multi-linear-model representation for nonlinear plants is proposed in this paper. The corresponding reference trajectories with respect to multiple models are presented. On these bases, the nonlinear multi-model predictive control schema is developed.
Yu-Geng Xi, Fan Wang, Guo-Hua Wu
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Nonlinear Model Predictive Control
2018This chapter presents a model-based strategy for a PHEV with the use of MPC concept. MPC appears to be an appropriate scheme to utilize contemporary concept potentials and to satisfy the automotive requisites, as most can be defined in the form of a constrained multi-input, multi-output optimal control problem, and MPC provides approximate resolution ...
Amir Taghavipour +2 more
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Nonlinear Model Predictive Path-Following Control
2009In the frame of this work, the problem of following parametrized reference paths via nonlinear model predictive control is considered. It is shown how the use of parametrized paths introduces new degrees of freedom into the controller design. Sufficient stability conditions for the proposed model predictive path-following control are presented.
Faulwasser, T., Findeisen, R.
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Nonlinear Model-Based Predictive Control
IFAC Proceedings Volumes, 2003Abstract In this paper a predictive control designed for the non-linear plant is discussed. The fuzzy-neuro internal model control (FNIMC) and predictive control based on the dynamic matrix control (DMC) algorithm are compared and designed. The proposed DMC and FNIMC used to the control the non-linear process.
Jana Paulusová +2 more
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Efficient nonlinear model predictive control
Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000For large numbers of degrees of freedom and/or high dimensional systems, nonlinear model predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which deploys the closed-loop paradigm that has proved effective for the case of linear time-varying or uncertain systems. The various attributes and
M. Cannon +3 more
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Nonlinear Model Predictive Control
2012A nonlinear model predictive control (NMPC) strategy requires the formulation of an optimization problem. For online NMPC the nonlinear programming problem must be solved numerically at every sampling interval, while explicit NMPC assumes that an explicit representation of the solution can be computed using multi-parametric nonlinear programming.
Alexandra Grancharova, Tor Arne Johansen
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