Time delays, model uncertainties, faults, and disturbances are frequently the main causes of performance degradation and instability in industrial process control. This article presents a fault-tolerant robust model-predictive control design for processes that involve the above effects and process constraints. The uncertainties are modeled into a polytopic affine form based on variations in the system's parameters. A new model based on augmentation of the state variables and tracking error is used that provides increased degrees of freedom for the control design and guarantees tracking performance. A parameter-dependent Lyapunov–Krasovskii functional is used to design a state-feedback control that reduces the conservatism of the conventional approach and ensures robust stability and tracking performance of the closed-loop system. At each sampling instant, a control action is computed by solving an online constrained optimization problem that minimizes the upper bound of the “worst-case” performance index. Finally, the proposed framework is employed for the fault-tolerant control of a continuous stirred tank reactor to demonstrate its effectiveness in mitigating actuator faults and tracking the desired set point.