On Multi-parametric Nonlinear Programming and Explicit Nonlinear Model Predictive Control
نویسنده
چکیده
A numerical algorithm for approximate multi-parametric nonlinear programming is developed. It allows approximate solutions to nonlinear optimization problems to be computed as explicit piecewise linear functions of the problem parameters. In control applications such as nonlinear constrained model predictive control this allows efficient online implementation in terms of an explicit piecewise linear state feedback without any real-time optimization.
منابع مشابه
Dual-mode Explicit Output-feedback Predictive Control Based on Neural Network Models ⋆
This paper applies an approximate multi-parametric Nonlinear Programming approach to explicitly solve output-feedback Nonlinear Model Predictive Control (NMPC) problems for constrained nonlinear systems described by black-box models. In particular, neural network models are used and the optimal regulation problem is considered. A dual-mode control strategy is employed in order to achieve an off...
متن کاملOptimal Decompression Through Multi-parametric Nonlinear Programming ★
Recently, a comprehensive dynamic mathematical model named Copernicus has been established to discover the mechanism of the vascular bubble formation and growth during and after decompression from a dive. The model uses Venous Gas Emboli (VGE) as a measurement and connects it to the risk of severe Decompression Sickness (DCS). Being validated by a series of diving tests, Copernicus model is bel...
متن کاملHybrid model predictive control of a nonlinear three-tank system based on the proposed compact form of piecewise affine model
In this paper, a predictive control based on the proposed hybrid model is designed to control the fluid height in a three-tank system with nonlinear dynamics whose operating mode depends on the instantaneous amount of system states. The use of nonlinear hybrid model in predictive control leads to a problem of mixed integer nonlinear programming (MINLP) which is very complex and time consuming t...
متن کاملRejection of the Feed-Flow Disturbances in a Multi-Component Distillation Column Using a Multiple Neural Network Model-Predictive Controller
This article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (MPC) of a chemical plant. A combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays. An optimization procedure for a neural MPC algorithm based on this model is then developed. T...
متن کاملExplicit Solution of Regulation Control Problems for Nonlinear Systems with Quantized Inputs
In this paper, a regulation control problem for constrained nonlinear systems with quantized inputs is formulated as a Model Predictive Control (MPC) problem. The MPC problem is represented as a multi-parametric Nonlinear Integer Programming (mp-NIP) problem and a computational method to find an explicit approximate solution of this problem is considered. It consists in constructing a feasible ...
متن کامل