We address the problem of tracking and stabilization of dissipative distributed parameter systems, by designing static output feedback controllers using adaptive proper orthogonal decomposition methodology (APOD). Initially, an ensemble of eigenfunctions is constructed based on a relatively small data ensemble which is then recursively updated as additional process data becomes available periodically. The proposed APOD methodology relaxes the need for a representative ensemble of snapshots (in the sense that it contains the global dynamics of the process). An accurate reduced-order model (ROM) is constructed and periodically refined based on these updated eigenfunctions. Using the ROM and continuous measurements available from the restricted number of sensors, a static output feedback controller is subsequently designed. This controller is successfully used to achieve the desired control objective of stabilization and tracking in the Kuramoto-Sivashinksy and FitzHugh-Nagumo equations.