Robust model reference adaptive controller for atmospheric plasma spray process

Abstract We add the σ -modification and low-frequency learning to model reference adaptive controller (MRAC) (Guduri et al. in SN Appl Sci 3:1–21, 2021) make it robust presence of two simultaneous bounded disturbances maintain consistent mean particles’ temperature velocity collectively called states (MPSs) when they impact substrate be coated. The MPSs affect coating quality. Even though results are applicable several processes, we consider an atmospheric plasma spray process (APSP). It is shown that proposed can quickly adopt average injection powder particles arc voltage change input current, argon hydrogen flow rates constant values MPSs. effects parameter MRAC, MRAC with $$\sigma$$ σ (R-MRAC), R-MRAC (MR-MRAC) schemes on tracking error convergence, steady-state error, disturbance rejection overshoot have been studied. numerical experiments suggest $$2 \le \gamma 20,$$ 2 ≤ γ 20 , $$10 \sigma 100,$$ 10 100 $$20 \lambda 80$$ λ 80 for MR-MRAC provide fast adaptation, no overshoot, low controlled response. $$\lambda > 0$$ > 0 suppresses high-frequency oscillations closed-loop control system, $$\gamma$$ serves tune gains. scheme has tested using software, LAVA-P, simulates well APSP.