Disturbance Rejection Improvement for the Sliding Mode Smith Predictor Based on Bio-inspired Tuning
This paper addresses a strategy to improve disturbance rejection for the Sliding Mode Controller designed in a Smith Predictor scheme (SMC-SP), with its parameters tuned through the bio-inspired search algorithm—Particle Swarm Optimization (PSO). Conventional SMC-SP is commonly based on tuning equations derived from step response identification, when First Order Plus Dead Time models (FOPDT) are considered and therefore controller parameters are previously set. Online PSO tuning based on minimization of the Integral of Time Absolute Error (ITAE) can provide faster recovery from external disturbances without significant increase of energy consumption, and the Sliding Mode feature deals with possible model mismatch. Simulation results for time delayed systems corroborating these benefits are presented.
KeywordsSmith predictor Sliding modes Disturbance rejection Time delay systems Particle swarm optimization
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