Soberness-in terms of electrical power-of data centers and high-performance computing systems is becoming an important design issue, as the global energy consumption of information technologies is rising at considerable levels. This issue is all the more complex as these systems are increasingly heterogeneous and variable in their behavior, for example, w.r.t. performance and power consumption, and less predictable, thus demanding runtime management and feedback control. This paper addresses the problem of the control of the power allocated to processors and hence their energy consumption and performance. The use of feedback control allows the energy consumption to be reduced by decreasing the speed without losing performance, by exploiting periods where read/write operations slow the progress. Previous works present limitations regarding both modeling (nonlinear models with numerous parameters) and control performance (mainly instability caused by platform variations). We develop a novel adaptive control that is robust to the variety of execution platforms while maintaining the existing global goals of energy management. We evaluate-on a real system using the Grid'5000 testbed-the robustness of the control to changes in initial parameters and to disturbances, and we compare it with the previous proportionalintegral (PI) control. Our adaptive control approach allows for up to 25% energy savings.