Event-triggered adaptive dynamic programming based optimal control for hydraulic servo actuator

Abstract

This paper considers adaptive optimal control for hydraulic servo actuators (HSAs) with unknown dynamics. A hydraulic servo actuator is a highly complex nonlinear system with parameters that cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. An event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback is considered. The control law is learned online based on measured input and output data instead of unmeasurable states and unknown system parameters. An event-based feedback strategy is introduced to the closed-loop system to save computing and communication resources and reduce the number of control updates. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSA.