QSID-MPC: Model Predictive Control With System Identification From Quantized Data

Cloud-assisted system identification and control have emerged as practical solutions for low-power, resource-constrained control systems such as micro-UAVs. In a typical cloud-assisted setting, state and input data are transmitted from local agents to a central computer over low-bandwidth wireless links, leading to quantization. This letter investigates the impact of state and input data quantization on system identification and subsequent Model Predictive Controller (MPC). We establish a fundamental relationship between the quantization resolution and the resulting model error, and analyze how this error propagates to affect the stability and boundedness of the MPC tracking error. In particular, we show that, given a sufficiently rich dataset, the model error is bounded as a function of the quantization resolution, and the MPC tracking error is likewise ultimately bounded.