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In this paper, we propose a robust constrained model predictive control (RCMPC) for stabilizing processes with norm-bounded uncertainty. This type of uncertainty is used to avoid on-line computational burdens. The robust stability comes with a guarantee described by parameter-dependent Lyapunov function (PDLF). The control law based on PDLF is potentially less conservative than that based on single Lyapunov function (SLF), due to additional degree of freedom. We give numerical examples based on a single non-isothermal CSTR system to illustrate the effectiveness of this algorithm.