Self-tuning control based on multi-innovation stochastic gradient parameter estimation

This paper uses the multi-innovation stochastic gradient (MISG) algorithm to estimate the parameters of discrete-time systems, and presents an MISG based self-tuning control scheme. Furthermore, we prove that the parameter estimation error converges to zero under persistent excitation, and the parameter estimation based control algorithm can asymptotically achieve virtually optimal control, and ensure that the closed-loop systems are stable and globally convergent. The simulation example is included.