Smooth switching control for discrete-time multi-variable systems with unknown time-varying parameters

For a class of discrete-time multi-variable systems with time-varying parametric uncertainty, the parameter identification method and switching control architecture are developed in this study to implement smooth switching control. Specifically, the identification algorithm based on the least geometric mean squares is derived for the multi-variable system. In addition, the switching control system, with model reference control technique as the basis of control design for each local model, is proposed to achieve satisfactory control performance, especially to eliminate the oscillations at the switching boundaries which can be attributed to the difference in the input-output dynamics between the pre- and post-switching closed-loop subsystems. Furthermore, the switching strategy based on neural network classifier is put forward for the accurate switching among a set of model reference controllers which are correspondingly designed for the identified subsystems. Finally, the feasibility and effectiveness of the proposed identification and control schemes are verified by numerical simulations, which show that the desirable identification and control performances are guaranteed under the proposed schemes. Moreover, the advantage of the presented switching control system in terms of smooth switching is validated through comparison with other kinds of control schemes.