Learning-Based SMPC for Reference Tracking Under State-Dependent Uncertainty: An Application to Atmospheric Pressure Plasma Jets for Plasma Medicine
Learning-Based SMPC for Reference Tracking Under State-Dependent Uncertainty: An Application to Atmospheric Pressure Plasma Jets for Plasma Medicine
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DOI：
10.1109/tcst.2021.3069825
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发表时间：
2022-03
影响因子：
4.8
通讯作者：
Angelo D. Bonzanini;D. Graves;A. Mesbah
中科院分区：
文献类型：
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作者：
Angelo D. Bonzanini;D. Graves;A. Mesbah
现代技术系统的复杂性日益增加，加剧了基于模型控制中的模型不确定性，对闭环系统的安全有效运行提出了巨大挑战。模型不确定性的在线学习可以通过减少对象模型失配来提高控制性能。提出了一种基于学习的随机模型预测控制（LB-SMPC）策略，用于具有附加状态相关不确定性的随机线性系统的参考跟踪。LB-SMPC策略在线自适应状态相关的不确定性模型，以减少控制性能优化的对象模型失配。标准的可达性和统计工具的杠杆沿着与状态依赖的不确定性模型，以开发一个机会约束收紧的方法，确保状态约束满足的概率。LB-SMPC策略的稳定性和递归的可行性建立跟踪时变目标，而不需要重新设计的控制器每次改变的目标。在大气压等离子体射流（APPJ）试验台上，实验证明了LB-SMPC策略的性能，并在等离子体医学和材料加工中具有原型应用。与基于学习的MPC没有不确定性处理和无偏移MPC的实时控制比较展示了LB-SMPC用于具有难以建模和/或时变动态的安全关键系统的预测控制的有用性。
The increasing complexity of modern technical systems can exacerbate model uncertainty in model-based control, posing a great challenge to safe and effective system operation under closed loop. Online learning of model uncertainty can enhance control performance by reducing plant–model mismatch. This article presents a learning-based stochastic model predictive control (LB-SMPC) strategy for reference tracking of stochastic linear systems with additive state-dependent uncertainty. The LB-SMPC strategy adapts the state-dependent uncertainty model online to reduce plant–model mismatch for control performance optimization. Standard reachability and statistical tools are leveraged along with the state-dependent uncertainty model to develop a chance constraint-tightening approach, which ensures state constraint satisfaction in probability. The stability and recursive feasibility of the LB-SMPC strategy are established for tracking time-varying targets, without the need to redesign the controller every time the target is changed. The performance of the LB-SMPC strategy is experimentally demonstrated on an atmospheric pressure plasma jet (APPJ) testbed with prototypical applications in plasma medicine and materials processing. Real-time control comparisons with learning-based MPC with no uncertainty handling and offset-free MPC showcase the usefulness of LB-SMPC for predictive control of safety-critical systems with hard-to-model and/or time-varying dynamics.