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Based on an equivalent two-dimensional (2D) Fornasini-Marchsini (2D-FM) model description of a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper first introduces relevant concepts of the fault-tolerant guaranteed cost control, and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of setpoint trajectory in both the time and batchwise directions. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic ILC controller consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along both the time and the cycle directions but also satisfy the H刅 performance level and a cost function having upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions of the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which are formulated a convex optimization problem with LMI constraints, are presented, too. Finally, an example of injection molding is given to illustrate the effectiveness and advantages of the proposed ILRGCC design approach..