Quantization has been demonstrated to be an effective approach for conserving communication resources in networked control systems. By converting analog signals into digital signals with finite levels, it reduces data transmission while maintaining control performance. Building on the dynamic ETSs examined in the previous two chapters, which utilizes adjustable triggering thresholds to balance communication efficiency and system performance, this chapter will investigate the integration of quantization with event-triggered SMC for IT2 T-S fuzzy systems under communication constraints. While static quantization strategies quantize signals using fixed quantizer parameters (Coutinho et al. in IEEE Trans Autom Control 55(3):761–766, 2010 [2]; Fu and Xie in IEEE Trans Autom Control 50(11):1698–1711, 2005 [1]), dynamic quantization offers superior adaptability by adjusting quantization levels in real time to reduce quantization errors, as highlighted in prior works.