Salt Julián, Alcaina José, Cuenca Ángel, Baños Alfonso
Systems Eng. and Control Dept., Instituto de Automatica e Informatica Industrial, Universitat Politecnica de Valencia, Cno. Vera, s/n, E-46022 Valencia, Spain.
Informatics and Systems Department, Universidad de Murcia, Campus de Espinardo, E-30071 Murcia, Spain.
ISA Trans. 2020 Jun;101:130-146. doi: 10.1016/j.isatra.2020.01.025. Epub 2020 Jan 16.
When in a digital control strategy there are samples lost due to limitations, different multirate (MR) control options can be used for solving the problem: Dual-rate inferential control (IC) and model-based dual-rate control (MBDR). The objective of this contribution is to analyze, compare, and to assess their behavior under different perspectives. Is a dual-rate inferential control better than a model-based dual-rate control? Both options lead to a periodically time-varying discrete-time system and for this reason a lifted modeling is considered. An efficient algorithm is used for computing a MR system's frequency response for these control structures. The robust performance and disturbance effects are studied in detail under sample losses and process uncertainty, and some considerations are reported. A new QFT (quantitative feedback theory) procedure for dual-rate systems analysis is also described. Analysis and simulation examples and experimental results for UGV path tracking are introduced in this work, revealing that MBDR outperforms IC when the model contains important uncertainties.
当数字控制策略中由于限制导致样本丢失时，可以使用不同的多速率（MR）控制选项来解决该问题：双速率推断控制（IC）和基于模型的双速率控制（MBDR）。本文的目的是从不同角度分析、比较和评估它们的性能。双速率推断控制比基于模型的双速率控制更好吗？这两种选项都会导致周期性时变离散时间系统，因此考虑采用提升建模。使用一种高效算法来计算这些控制结构的MR系统频率响应。在样本丢失和过程不确定性的情况下，详细研究了鲁棒性能和干扰影响，并给出了一些考虑因素。还描述了一种用于双速率系统分析的新的定量反馈理论（QFT）程序。本文介绍了分析和仿真示例以及无人地面车辆路径跟踪的实验结果，结果表明当模型包含重要不确定性时，MBDR的性能优于IC。
