In this paper, we combine self-optimizing control and extremum-seeking control in the context of real-ime optimization. Self-optimizing control is based on controlling a single measurement or measurement combination whose optimal setpoint is insensitive to the expected disturbances. This gives a fast reaction to disturbances, but the optimal setpoint may change over time because of larger deviations from the nominal optimal point or due to unmodelled disturbances. Extremum-seeking control, on the other hand, belongs to a class of model-free methods that optimizes the system based on directly measuring the cost. However, it converges slowly. In this paper, we propose to use an extremum-seeking controller to provide setpoint adjustments to the self-optimizing controller in order to improve the convergence rate. The key idea here is to keep the process near the optimal region on a fast timescale using self-optimizing control and fine tune the setpoint on a longer timescale using extremum-seeking control. We verify the proposed method using an ammonia reactor case study. (C) 2019 Elsevier Ltd. All rights reserved.