Trading quantization precision for update rates for systems with limited communication in the uplink channel

In many situations, control applications have to exchange information through limited bandwidth communication channels, which affect their behavior. For that reason, there is a strong need for methods that maximize the relevancy of the exchanged control signals. In general, increasing control signals’ update frequency improves the disturbance rejection abilities whereas increasing their quantization precision improves the steady state performance. However, when the bandwidth is limited, increasing the update frequency necessitates the reduction of the quantization precision and vice versa. Motivated by these observations, and focusing on the uplink bandwidth limitations, an approach for the dynamical online state feedback assignment of control inputs’ quantization precision and update rate is proposed. This approach, which is based on the model predictive control technique, enables us to choose the update rate and the quantization levels of control signals from a predefined set, in order to optimize the control performance. Practical stability properties of the approach are then studied. Finally, the effectiveness of the proposed method is illustrated on a simulation example.