Real-time embedded systems often require the ability of adaptiveness and robustness, because their interactions with physical environments dynamically change workloads. Multi-core chips are becoming an ideal candidate hardware component for such environments, since each of them carries two or more cores on a single die, and has potential for providing execution parallelism as well as better performance at low cost. Parallelism, on the other hand, necessitates complex analysis of computation problems, such as task scheduling, while improving the realization of adaptive controls. Pfair is an optimal scheduling algorithm that can fully utilize all cores in the system, but it incurs excessive scheduling overheads which, in turn, diminishes its practicality in embedded systems. To mitigate this problem, the hybrid partitioned-global Pfair (HPGP) scheduler was proposed in previous work, which significantly reduces the number of task migrations and global scheduling points by performing global scheduling only when absolutely necessary, while still achieving full processor utilization. In this paper, the HPGP scheduler is further extended to support the adaptive controls to dynamic real-time task systems. Experimental evaluation results have shown that the extended HPGP can successfully handle dynamic task systems, thus making it suitable for embedded real-time systems.