Data-Driven Model Predictive Control for Unknown Nonlinear NCSs With Stochastic Sampling Intervals and Successive Packet Dropouts

Due to the unreliable communication network, networked control systems (NCSs) are often subjected to communication imperfections including stochastic sampling intervals (SSIs) and packet dropouts, which can lead to degradation of control performance and even jeopardize the stability of the NCSs. In this article, the data-driven model predictive control (DMPC) strategy is proposed to stabilize a class of unknown nonlinear NCSs with SSIs and successive packet dropouts (SPDs). First, an equivalent stochastic sampling model is constructed by capturing the randomness of both SSIs and SPDs, which can determine the probability information of the equivalent sampling interval between consecutive no-packet -dropout update instants. Subsequently, a multimodel predictive structure is designed based on the possible lengths of the sampling interval in the equivalent stochastic sampling model, which can provide predictive outputs for the subsequent controller design. Furthermore, to reduce the computational burden associated with the prolongation of the prediction horizon in the multimodel predictive structure, a prediction data interpolation algorithm based on the Lagrange interpolation polynomial is introduced. Additionally, to ensure the accuracy of interpolation prediction output, an adaptive mechanism for updating interpolation nodes is designed to dynamically adjust the number and position of these nodes. Finally, a cost function that relies on the expectation of the predictive output is designed and solved to achieve stable tracking control of the considered NCSs. The stability of the DMPC strategy is demonstrated in detail. Numerical examples and industrial applications for the wastewater treatment process (WWTP) demonstrate that DMPC can obtain satisfied control performance.