模型预测控制：基于离散、连续、线性和非线性系统的Matlab开发

简介：
Model Predictive Control (MPC) forecasts and optimizes the dynamic behavior of time-varying processes over a future time horizon. This control package accepts both linear and nonlinear models, utilizing large-scale nonlinear programming solvers like APOPT and IPOPT to address data reconciliation, moving horizon estimation, real-time optimization, dynamic simulation, and nonlinear MPC challenges. Within this directory are three example files designed for implementing Linear Time-Invariant (LTI) controllers: 1. A step-by-step guide for converting any LTI model into the APM format. 2. An example demonstrating how to validate the accuracy of a model through step response analysis. 3. A tutorial on adjusting setpoints for new target values in an MPC framework. Steps 2 and 3 also provide access to web interfaces for viewing results and controller responses directly. Additional resources, including tutorials and webinars focused on advanced applications like Unmanned Aerial Vehicles (UAVs), friction Stir Welding (FSW), biological systems, energy storage, combustion, fuel cells, and more details about these topics are available at the following link: http://apmonitor.com/wiki Previous sessions have covered subjects such as autonomous underwater vehicles (AUVs), friction stir welding (FSW), biological systems, energy storage, combustion, fuel cells and other cutting-edge applications.