Coordinated decentralised hybrid adaptive output feedback fuzzy control for a class of large-scale non-linear systems with strong interconnections

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

“…(32) can be written in the Brunovsky form: (35) where v = f (x,t) + g(x,t)(u +d) is the control input for the linearized model, andd denotes additive input disturbances. Thus one can use that …”

“…On the one side, adaptive fuzzy control has been proven to be an efficient nonlinear control method [31][32][33][34][35]. On the other side, differential flatness theory stands for a major direction in the design of nonlinear control systems [36][37][38][39][40][41][42][43][44][45].…”

Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

“…However, the intelligent control algorithms take full state feedback schemes. In contrast, decentralized output feedback fuzzy or neural control schemes were introduced for interrelated nonlinear systems in the papers [36][37][38][39][40][41][42][43][44]. Notwithstanding, there exist the following disadvantages in the matter of practical application.…”

“…Secondly, although the decentralized IAFC or DAFC syntheses have been introduced predicated upon H ∞ tracking control methodology in [39][40][41], they still cannot dispense with the conservative assumption that the modeling error stays squared integrable. Thirdly, it is worthwhile to be noted in the articles [36,37,41,42] that either cooperation or communication is required between all the individual subsystems such that none of the proposed decentralized control algorithms numbers among strict (or classical) decentralized control technique in nature. Furthermore, whether or not the decentralized control design in [41] is workable to a large degree is contingent upon the further assumption that there exist positive definite solutions for constructed Riccati-like matrix equation system.…”

“…There are three main contributions that are worthy to be pointed up. Firstly, unlike the previous coordinated decentralized controller design presented in [42] which needs to presuppose that the local controllers can share the a priori information about their individual reference models, the decentralized output feedback and adaptation mechanisms proposed for each subsystem hinges just upon its individual output, regardless of any other output reference. Secondly, the stability analysis of the closed-loop interactive system and the implementation of the output feedback mechanism are tackled without recourse to both the famous SPR condition and the conventional HGO as in the present literature [13, 14, 17-22, 26, 39-41], etc., so that the aforementioned disadvantages consequent upon them can be completely shunned.…”