Nonlinear model predictive control theory and algorithms .pdf download

In this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. We start by defining a basic NMPC algorithm for constant reference and continue by formalizing Estimated Reading Time: 5 mins. The predictive filter is used to estimate the position and velocity of nonlinear mass-damper-spring system. Results using this new algorithm indicate that the real- time predictive filter provides accurate estimates in the presence of highly nonlinear dynamics and significant errors in the model parameters. Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in the automotive industry, which use higher data sampling rates. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems.

1 Introduction. Model Predictive Control (MPC) is a very powerful control method, with widespread industrial application. The core idea of MPC [] is simple enough: a process model is used to predict the future output response of the process; then, at each instant, the control law is found through the solution of an online optimization problem, which is written in terms of the model, the. A new method is used to solve the nonconvex optimization problem of the nonlinear model predictive control (NMPC) for Hammerstein model. Using nonlinear models in MPC leads to a nonlinear and nonconvex optimization problem. Since control performances depend essentially on the results of the optimization method, in this work, we propose to use the filled function as a global optimization method. Predictive control strategy 1 A model predictive control law contains the basic components of prediction, optimization and receding horizon implementation. A summary of each of these ingredients is given below. Prediction The future response of the controlled plant is predicted using a dynamic model.

Economic nonlinear model predictive control is the common name for NMPC schemes in which the stage cost does not penalize the distance to a predefined equilibrium, which was one of the key. ear model predictive control schemes on the one hand and numerical algorithms on the other hand; for a comprehensive description of the contents we refer to Sect. As such, the book is somewhat more theoretical than engineering or application ori-ented monographs on nonlinear model predictive control, which are furthermore. Predictive control strategy 1 A model predictive control law contains the basic components of prediction, optimization and receding horizon implementation. A summary of each of these ingredients is given below. Prediction The future response of the controlled plant is predicted using a dynamic model.