Output Feedback Anti-Disturbance Control for Slow Sampled-Data Systems Combined with Model Predictive Control: An Alternating Predictive Observer Approach for Accelerating Sampling Rates

This paper investigates the combination of output feedback anti-disturbance control with model predictive control for slow-rate sampled-data systems with non-vanishing disturbances. The study focuses on a specific class of dual-rate systems, where the sensor samples output data at a slow rate, and the controller updates input data at a fast rate. To address the challenge of slow sampling, the paper introduces an observation method suitable for the anti-disturbance alternating predictive observer, utilizing only the measured output. To address the challenge of slow sampling, the paper introduces an observation method suitable for the anti-disturbance alternating predictive observer, utilizing only the measured output. By matching the observer's observation rate with the rapidly updated controller's rate, the sampling rate of the system can be improved. The proposed observer incorporates the estimated state and disturbance information into the output prediction. This information, combined with the control variable constraints in the resulting rolling horizon optimization problem, enables the design of a fast updating predictive control law. To evaluate the effectiveness of the proposed observer and the composite anti-disturbance control method under constrained control law, two sets of simulations are conducted on a dc-dc buck converter.