On the performance improvement of the optimal-sampling-inspired self-triggered control at implementation stage

Abstract

The self-triggered control includes a sampling strategy that focuses on decreasing the use of computational resources (processor and network) while preserving the same control performance as the one obtained via a controller with periodic sampling. This framework recently developed a self-triggered control technique inspired by a sampling pattern whose optimal density minimizes the control cost. This approach is called “optimal-sampling-inspired self-triggered control.” However, the strategies used to implement it on microprocessor-controlled systems working under perturbation are still unclear; this paper addresses some techniques to organize and improve the implementation on actual controllers. The proposed solution comprises the formulation of two algorithms to organize the implementation and the insertion of a closed-loop observer to deal with the perturbation that usually appears on natural plants. Regarding the former, specific computationally expensive processes involved in implementing this control technique are treated through their replacement by lightweight polynomials fitted at the design stage. Simulations and practical experiments confirm the solution is effective, and there could be an open research topic concerning observation in optimal-sampling self-triggered control strategies.