Design of Constrained Model-Predictive Control Systems with Increased Autonomy

约束模型预测控制（Constrained model predictive control， MPC）是一种利用在线优化方案中的过程模型来满足输入和输出受限过程的各种控制目标的技术。MPC使用的模型与现实世界之间总是存在差异。这种差异可能是由于MPC系统使用的初始模型不准确，或由于过程随时间的变化而产生的。提高MPC的自主性是该项目的主要目的，指的是MPC系统能够在最少的人为干预下处理流程模型可能不准确的各种情况。在本研究中将解决的情况是：o时不变过程，其中一个不准确的线性或非线性模型和不准确性的界限是可用的或需要开发的。o约束MPC反馈下的时变过程。更具体地说，将研究以下问题：如何构建和调整MPC系统（离线或在线），以确保在一定程度上过程/过程模型不匹配的情况下具有稳健的性能。相反，多少建模精度需要稳健的MPC性能。o监视其性能的受限MPC系统如何在必要时进行自我修改，例如，通过执行同步控制和闭环过程识别。

Abstract - Nikolaou - 9615979 Constrained model predictive control (MPC) is a technique that uses a process model in an on-line optimization scheme to meet various control objectives for a process whose inputs and outputs are constrained. There are always discrepancies between the model that MPC uses and the real world. Such discrepancies may arise from inaccuracies in the initial model used by an MPC system, or from variation of a process with time. Increased MPC autonomy, the main thrust of this project, refers to the capability of an MPC system to handle, with minimal human intervention, a wide range of situations where a process model could be inaccurate. Situations that will be addressed in this research are: o Time-invariant processes for which an inaccurate linear of nonlinear model and bounds on the inaccuracy are available or need to be developed. o Time-varying processes under constrained MPC feedback. More specifically, the following problems will be investigated: o How MPC systems can be structured and tuned (off- or on-line) to ensure robust performance for a certain level of process/process-model mismatch. Conversely, how much modeling accuracy is required for robust MPC performance. o How a constrained MPC systems that monitors its performance, can modify itself if necessary, e.g., by performing simultaneous control and closed-loop process identification.