The paper presents a novel design methodology for an intelligent controller based on a database capable of self-study and auto-control. The motivation stems from making a smart database analogous to human brain, namely, a dummy brain database (DBD). The data stored in the DBD may be collected from a plant/system at present, recent past and/or over a period of time in the past, i.e. the historical data. The proposed method intends to enable DBD to perform information processing, knowledge elicitation and decision making in a manner similar to human brain. In particular the features of human memory including its sensitivity, frequency of change, information update and reconstruction are researched and the mechanism of the brain's associative memories studied. The DBD thus established has led to a self-study and autocontrol model which is capable for learning over the machine's lifetime .Possible applications in control of a group of elevators and in city traffic control are discussed.