基于状态观测器的事件触发预测控制
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摘要
针对一类存在数据丢包的网络控制系统,在系统状态无法直接测量的情况下,基于状态观测器和事件触发机制,提出了一种事件触发预测控制算法。首先,在传感器端建立了基于输出信息的事件触发机制1,用来减少传感器端到控制器端的信息发送次数,减少数据量的发送。其次,在控制器端建立事件触发机制2和状态观测器,用来重构和预测系统的状态和未来的控制输入序列,并发送给执行器端。执行器从接收到的序列中选择合适的控制输入作用于被控对象。然后利用Lyapunov稳定性理论,结合线性矩阵不等式,同时设计出反馈矩阵和事件触发机制中的参数。最后,通过仿真验证了所提方法的有效性。
For a class of network control systems with packet dropouts,an event-triggered predictive control algorithm is proposed based on state observer and event triggering mechanism when the system states are immeasurable directly.Firstly,an event trigger mechanism 1 based on output information is established at the sensor end to reduce the number of sensor-to-controller message transmissions. Secondly, an event trigger mechanism 2 and a state observer are built on the controller side to reconstruct and predict the state of the system and future control inputs. Based on the Lyapunov stability theory combined with linear matrix inequality(LMI) technique,the feedback matrix and event trigger mechanism parameters are designed. Finally,the effectiveness of the proposed method is verified by simulation.
For a class of network control systems with packet dropouts,an event-triggered predictive control algorithm is proposed based on state observer and event triggering mechanism when the system states are immeasurable directly.Firstly,an event trigger mechanism 1 based on output information is established at the sensor end to reduce the number of sensor-to-controller message transmissions. Secondly, an event trigger mechanism 2 and a state observer are built on the controller side to reconstruct and predict the state of the system and future control inputs. Based on the Lyapunov stability theory combined with linear matrix inequality(LMI) technique,the feedback matrix and event trigger mechanism parameters are designed. Finally,the effectiveness of the proposed method is verified by simulation.
引文
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[3] Wong JKW,Li H,Wang SW. Intelligent building research:a review[J]. Automation in Construction,2005,14(1):143-159.
[4] Nguyen Van Hung. On the stability of the solution mapping for parametric traffic network problems[J].Indagationes Mathematicae,2018,29(3):885-894.
[5] Li H,Yan W. Model predictive stabilization of constrained underactuated autonomous underwater vehicles with guaranteed feasibility and stability[J].IEEE/ASME Transactions on Mechatronics,2017,22(3):1185-1194.
[6] QIU Li,YAO Feng-qi,XU Gang,et al. Output feedback guaranteed cost control for networked control systems with random packet dropouts and time delays in forward and feedback Communication Links[J]. IEEE Transactions on Automation Science and Engineering,2016,13(1):284-295.
[7] QIU Jian bin,GAO Hui jun,STEVEN X D. Recent advances on fuzzy-model-based nonlinear networked control Systems:a survey[J]. IEEE Transactions on Industrial Electronics,2016,63(2):1207-1217.
[8]陈勇,李猛.网络控制系统的预测控制算法研究进展[J].电子科技大学学报,2016,45(4):546-572.
[9] Wang Liuping,Chris Freeman,Eric Rogers. Observer-based Predictive Repetitive Control with Experimental Validation[J]. 20th IFAC World Congress,2017,50(1):3674-3679.
[10]杨飞生,汪璟.基于事件触发机制的网络控制研究综述[J].控制与决策,2018,33(6):969-977.
[11] Chu Xiaoan,Li Muguo. Event-triggered fault estimation and sliding mode fault-tolerant control for a class of nonlinear networked control systems[J].Journal of the Franklin Institute,2018,355(13):5475-5502.
[12]尹秀霞.复杂网络化系统的事件触发控制研究[D].武汉:华中科技大学,2014.
[13] Felix Berkel,Benjamin Watkins,Steven L,et al.Output-Based Event-Triggered Model Predictive Control for Networked Control Systems[J].20th IFAC World Congress,2017,50(1):8951-8956.
[14] Min H,Wang S,Sun F,et al. Robust consensus for networked mechanical systems with coupling time delay[J].International Journal of Control Automation and Systems,2012,10(2):227-237.