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Due to the increasing complexity, the behavior of large-scale distributed systems becomes difficult to predict. The ability of online identification and auto-tuning of adaptive control systems has made the adaptive control theoretical design an attractive approach for quality of service (QoS) guarantee. However, there is an inherent constraint in adaptive control systems, i.e. a conflict between asymptotically good control and asymptotically good parameter estimates. This paper addresses these limitations via sensitivity analysis. The simulation study demonstrates that the adaptive control theoretical design depends on the excitation signal, environment uncertainty, and a priori knowledge on the system. In addition, this paper proposes an adaptive dual control framework for mitigating these constraints in QoS design. By incorporating the existing uncertainty of the online prediction into the control strategy, the dual adaptive control framework optimizes the tradeoff between the control goal and the uncertainty.