Khan Owais, El Mistiri Mohamed, Banerjee Sarasij, Hekler Eric, Rivera Daniel E
Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287 USA.
Center for Wireless & Population Health Systems, University of California, San Diego (UCSD), La Jolla, CA 92093 USA.
Control Eng Pract. 2025 Jan;154. doi: 10.1016/j.conengprac.2024.106171. Epub 2024 Nov 21.
This paper presents the formulation, design procedure, and application of a hybrid model predictive control (HMPC) scheme for hybrid systems that is embedded in a mixed logical dynamical (MLD) framework. The proposed scheme adopts a three degrees-of-freedom (3DoF) tuning method to accomplish precise setpoint tracking and ensure robustness in the face of disturbances (both measured and unmeasured) and uncertainty. Furthermore, the HMPC algorithm employs setpoint and disturbance anticipation to proactively enhance controller performance and potentially reduce control effort. Slack variables in the objective function prevent the mixed-integer quadratic problem from becoming infeasible. The effectiveness of the proposed algorithm is demonstrated through its application in three distinct case studies, which include control of production-inventory systems, time-varying behavioral interventions for physical activity, and management of epidemics/pandemic prevention. These case studies indicate that the HMPC algorithm can effectively manage hybrid dynamics, setpoint tracking and disturbance rejection in diverse and demanding circumstances, while tuned to perform well in the presence of nonlinearity and uncertainty.
本文介绍了一种嵌入混合逻辑动态（MLD）框架的混合系统混合模型预测控制（HMPC）方案的公式化、设计过程及应用。所提出的方案采用三自由度（3DoF）调谐方法来实现精确的设定值跟踪，并确保在面对干扰（包括可测量和不可测量的干扰）及不确定性时具有鲁棒性。此外，HMPC算法采用设定值和干扰预测来主动提高控制器性能，并有可能减少控制努力。目标函数中的松弛变量可防止混合整数二次问题变得不可行。通过在三个不同的案例研究中的应用，证明了所提出算法的有效性，这些案例研究包括生产库存系统的控制、身体活动的时变行为干预以及流行病/大流行预防的管理。这些案例研究表明，HMPC算法能够在各种苛刻情况下有效地管理混合动态、设定值跟踪和干扰抑制，同时在存在非线性和不确定性的情况下经过调谐可表现良好。
